Journal articles: 'SISR'

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Ozcinar, Cagri, and Aakanksha Rana. "Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views." Electronic Imaging 2021, no. 9 (January 18, 2021): 295–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-295.

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Omnidirectional images (ODIs), also known as 360-degree images, enable viewers to explore all directions of a given 360-degree scene from a fixed point. Designing an immersive imaging system with ODI is challenging as such systems require very large resolution coverage of the entire 360 viewing space to provide an enhanced quality of experience (QoE). Despite remarkable progress on single image super-resolution (SISR) methods with deep-learning techniques, no study for quality assessments of super-resolved ODIs exists to analyze the quality of such SISR techniques. This paper proposes an objective, full-reference quality assessment framework which studies quality measurement for ODIs generated by GAN-based and CNN-based SISR methods. The quality assessment framework offers to utilize tangential views to cope with the spherical nature of a given ODIs. The generated tangential views are distortion-free and can be efficiently scaled to high-resolution spherical data for SISR quality measurement. We extensively evaluate two state-of-the-art SISR methods using widely used full-reference SISR quality metrics adapted to our designed framework. In addition, our study reveals that most objective metric show high performance over CNN based SISR, while subjective tests favors GAN-based architectures.

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Plaza, D. A., R. De Keyser, G. J. M. De Lannoy, L. Giustarini, P. Matgen, and V. R. N. Pauwels. "The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter." Hydrology and Earth System Sciences 16, no. 2 (February 8, 2012): 375–90. http://dx.doi.org/10.5194/hess-16-375-2012.

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Abstract. The Sequential Importance Sampling with Resampling (SISR) particle filter and the SISR with parameter resampling particle filter (SISR-PR) are evaluated for their performance in soil moisture assimilation and the consequent effect on baseflow generation. With respect to the resulting soil moisture time series, both filters perform appropriately. However, the SISR filter has a negative effect on the baseflow due to inconsistency between the parameter values and the states after the assimilation. In order to overcome this inconsistency, parameter resampling is applied along with the SISR filter, to obtain consistent parameter values with the analyzed soil moisture state. Extreme parameter replication, which could lead to a particle collapse, is avoided by the perturbation of the parameters with white noise. Both the modeled soil moisture and baseflow are improved if the complementary parameter resampling is applied. The SISR filter with parameter resampling offers an efficient way to deal with biased observations. The robustness of the methodology is evaluated for 3 model parameter sets and 3 assimilation frequencies. Overall, the results in this paper indicate that the particle filter is a promising tool for hydrologic modeling purposes, but that an additional parameter resampling may be necessary to consistently update all state variables and fluxes within the model.

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Ai, Na, Jinye Peng, Xuan Zhu, and Xiaoyi Feng. "SISR via trained double sparsity dictionaries." Multimedia Tools and Applications 74, no. 6 (October 15, 2013): 1997–2007. http://dx.doi.org/10.1007/s11042-013-1736-x.

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Pek, Jun Wei, Ismail Osman, Mandy Li-Ian Tay, and Ruther Teo Zheng. "Stable intronic sequence RNAs have possible regulatory roles in Drosophila melanogaster." Journal of Cell Biology 211, no. 2 (October 26, 2015): 243–51. http://dx.doi.org/10.1083/jcb.201507065.

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Stable intronic sequence RNAs (sisRNAs) have been found in Xenopus tropicalis, human cell lines, and Epstein-Barr virus; however, the biological significance of sisRNAs remains poorly understood. We identify sisRNAs in Drosophila melanogaster by deep sequencing, reverse transcription polymerase chain reaction, and Northern blotting. We characterize a sisRNA (sisR-1) from the regena (rga) locus and show that it can be processed from the precursor messenger RNA (pre-mRNA). We also document a cis-natural antisense transcript (ASTR) from the rga locus, which is highly expressed in early embryos. During embryogenesis, ASTR promotes robust rga pre-mRNA expression. Interestingly, sisR-1 represses ASTR, with consequential effects on rga pre-mRNA expression. Our results suggest a model in which sisR-1 modulates its host gene expression by repressing ASTR during embryogenesis. We propose that sisR-1 belongs to a class of sisRNAs with probable regulatory activities in Drosophila.

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Weng, Yu, Zehua Chen, and Tianbao Zhou. "Improved differentiable neural architecture search for single image super-resolution." Peer-to-Peer Networking and Applications 14, no. 3 (January 14, 2021): 1806–15. http://dx.doi.org/10.1007/s12083-020-01048-4.

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AbstractDeep learning has shown prominent superiority over other machine learning algorithms in Single Image Super-Resolution (SISR). In order to reduce the efforts and resources cost on manually designing deep architecture, we use differentiable neural architecture search (DARTS) on SISR. Since neural architecture search was originally used for classification tasks, our experiments show that direct usage of DARTS on super-resolutions tasks will give rise to many skip connections in the search architecture, which results in the poor performance of final architecture. Thus, it is necessary for DARTS to have made some improvements for the application in the field of SISR. According to characteristics of SISR, we remove redundant operations and redesign some operations in the cell to achieve an improved DARTS. Then we use the improved DARTS to search convolution cells as a nonlinear mapping part of super-resolution network. The new super-resolution architecture shows its effectiveness on benchmark datasets and DIV2K dataset.

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Zhu, Xining, Lin Zhang, Lijun Zhang, Xiao Liu, Ying Shen, and Shengjie Zhao. "GAN-Based Image Super-Resolution with a Novel Quality Loss." Mathematical Problems in Engineering 2020 (February 18, 2020): 1–12. http://dx.doi.org/10.1155/2020/5217429.

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Single image super-resolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and generative adversarial networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with human vision system (HVS), we design a quality loss by integrating an image quality assessment (IQA) metric named gradient magnitude similarity deviation (GMSD). To our knowledge, it is the first time to truly integrate an IQA metric into SISR. Moreover, to overcome the instability of the original GAN, we use a variant of GANs named improved training of Wasserstein GANs (WGAN-GP). Besides GMGAN, we highlight the importance of training datasets. Experiments show that GMGAN with quality loss and WGAN-GP can generate visually appealing results and set a new state of the art. In addition, large quantity of high-quality training images with rich textures can benefit the results.

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Wang, Junwei, Kun Gao, Zhenzhou Zhang, Chong Ni, Zibo Hu, Dayu Chen, and Qiong Wu. "Multisensor Remote Sensing Imagery Super-Resolution with Conditional GAN." Journal of Remote Sensing 2021 (September 8, 2021): 1–11. http://dx.doi.org/10.34133/2021/9829706.

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Despite the promising performance on benchmark datasets that deep convolutional neural networks have exhibited in single image super-resolution (SISR), there are two underlying limitations to existing methods. First, current supervised learning-based SISR methods for remote sensing satellite imagery do not use paired real sensor data, instead operating on simulated high-resolution (HR) and low-resolution (LR) image-pairs (typically HR images with their bicubic-degraded LR counterparts), which often yield poor performance on real-world LR images. Second, SISR is an ill-posed problem, and the super-resolved image from discriminatively trained networks with lp norm loss is an average of the infinite possible HR images, thus, always has low perceptual quality. Though this issue can be mitigated by generative adversarial network (GAN), it is still hard to search in the whole solution-space and find the best solution. In this paper, we focus on real-world application and introduce a new multisensor dataset for real-world remote sensing satellite imagery super-resolution. In addition, we propose a novel conditional GAN scheme for SISR task which can further reduce the solution-space. Therefore, the super-resolved images have not only high fidelity, but high perceptual quality as well. Extensive experiments demonstrate that networks trained on the introduced dataset can obtain better performances than those trained on simulated data. Additionally, the proposed conditional GAN scheme can achieve better perceptual quality while obtaining comparable fidelity over the state-of-the-art methods.

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Pashaei, Mohammad, Michael J. Starek, Hamid Kamangir, and Jacob Berryhill. "Deep Learning-Based Single Image Super-Resolution: An Investigation for Dense Scene Reconstruction with UAS Photogrammetry." Remote Sensing 12, no. 11 (May 29, 2020): 1757. http://dx.doi.org/10.3390/rs12111757.

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The deep convolutional neural network (DCNN) has recently been applied to the highly challenging and ill-posed problem of single image super-resolution (SISR), which aims to predict high-resolution (HR) images from their corresponding low-resolution (LR) images. In many remote sensing (RS) applications, spatial resolution of the aerial or satellite imagery has a great impact on the accuracy and reliability of information extracted from the images. In this study, the potential of a DCNN-based SISR model, called enhanced super-resolution generative adversarial network (ESRGAN), to predict the spatial information degraded or lost in a hyper-spatial resolution unmanned aircraft system (UAS) RGB image set is investigated. ESRGAN model is trained over a limited number of original HR (50 out of 450 total images) and virtually-generated LR UAS images by downsampling the original HR images using a bicubic kernel with a factor × 4 . Quantitative and qualitative assessments of super-resolved images using standard image quality measures (IQMs) confirm that the DCNN-based SISR approach can be successfully applied on LR UAS imagery for spatial resolution enhancement. The performance of DCNN-based SISR approach for the UAS image set closely approximates performances reported on standard SISR image sets with mean peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index values of around 28 dB and 0.85 dB, respectively. Furthermore, by exploiting the rigorous Structure-from-Motion (SfM) photogrammetry procedure, an accurate task-based IQM for evaluating the quality of the super-resolved images is carried out. Results verify that the interior and exterior imaging geometry, which are extremely important for extracting highly accurate spatial information from UAS imagery in photogrammetric applications, can be accurately retrieved from a super-resolved image set. The number of corresponding keypoints and dense points generated from the SfM photogrammetry process are about 6 and 17 times more than those extracted from the corresponding LR image set, respectively.

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Ben Aouicha, Mohamed, Mohamed Ali Hadj Taieb, and Abdelmajid Ben Hamadou. "SISR: System for integrating semantic relatedness and similarity measures." Soft Computing 22, no. 6 (November 21, 2016): 1855–79. http://dx.doi.org/10.1007/s00500-016-2438-x.

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Cao, Kerang, Yuqing Liu, Lini Duan, and Tian Xie. "Adaptive Residual Channel Attention Network for Single Image Super-Resolution." Scientific Programming 2020 (August 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/8877851.

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Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively.

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Chang, Min, Shuai Han, Guo Chen, and Xuedian Zhang. "The Research on Enhancing the Super-Resolving Effect of Noisy Images through Structural Information and Denoising Preprocessing." AI 1, no. 3 (July 20, 2020): 329–41. http://dx.doi.org/10.3390/ai1030022.

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Both noise and structure matter in single image super-resolution (SISR). Recent researches have benefited from a generative adversarial network (GAN) that promotes the development of SISR by recovering photo-realistic images. However, noise and structural distortion are detrimental to SISR. In this paper, we focus on eliminating noise and geometric distortion during super-resolving noisy images. It includes a denoising preprocessing module and a structure-keeping branch. At the same time, the advantages of GAN are still used to generate satisfying details. Especially, on the basis of the original SISR, the gradient branch is developed, and the denoising preprocessing module is designed before the SR branch. Denoising preprocessing eliminates noise by learning the noise distribution and utilizing residual-skip. By restoring the high-resolution(HR) gradient maps and combining gradient loss with space loss to guide the parameter optimization, the gradient branch brings additional structural constraints. Experimental results show that we have obtained better Perceptual Index (PI) and Learned Perceptual Image Patch Similarity (LPIPS) performance on the noisy images, and Peak Signal to Noise Ratio(PSNR) and Structure Similarity (SSIM) are equivalent compared with the most reported SR method combined with DNCNN. Taking the Urban100 dataset with noise intensity in 25 as an example, four indexes of the proposed method are respectively 3.6976(PI), 0.1124(LPIPS), 24.652(PSNR) and 0.9481(SSIM). Combined with the performance under different noise intensity and different datasets reflected in box-and-whiskers plots, the values of PI and LPIPS are the best among all comparison methods, and PSNR and SSIM also achieve equivalent effects. Also, the visual results show that the proposed method of enhancing the super-resolving effect of noisy images through structural information and denoising preprocessing(SNS) is not affected by the noise while preserving the geometric structure in SR processing.

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Huang, Feng, Zhifeng Wang, Jing Wu, Ying Shen, and Liqiong Chen. "Residual Triplet Attention Network for Single-Image Super-Resolution." Electronics 10, no. 17 (August 27, 2021): 2072. http://dx.doi.org/10.3390/electronics10172072.

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Single-image super-resolution (SISR) techniques have been developed rapidly with the remarkable progress of convolutional neural networks (CNNs). The previous CNNs-based SISR techniques mainly focus on the network design while ignoring the interactions and interdependencies between different dimensions of the features in the middle layers, consequently hindering the powerful learning ability of CNNs. In order to address this problem effectively, a residual triplet attention network (RTAN) for efficient interactions of the feature information is proposed. Specifically, we develop an innovative multiple-nested residual group (MNRG) structure to improve the learning ability for extracting the high-frequency information and train a deeper and more stable network. Furthermore, we present a novel lightweight residual triplet attention module (RTAM) to obtain the cross-dimensional attention weights of the features. The RTAM combines two cross-dimensional interaction blocks (CDIBs) and one spatial attention block (SAB) base on the residual module. Therefore, the RTAM is not only capable of capturing the cross-dimensional interactions and interdependencies of the features, but also utilizing the spatial information of the features. The simulation results and analysis show the superiority of the proposed RTAN over the state-of-the-art SISR networks in terms of both evaluation metrics and visual results.

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Zha, Lei, Yu Yang, Zicheng Lai, Ziwei Zhang, and Juan Wen. "A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution." Electronics 10, no. 11 (May 22, 2021): 1234. http://dx.doi.org/10.3390/electronics10111234.

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In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems, researchers utilize dense skip connections to promote the model’s feature representation ability by reusing deep features of different receptive fields. Benefiting from the dense connection block, SRDensenet has achieved excellent performance in SISR. Despite the fact that the dense connected structure can provide rich information, it will also introduce redundant and useless information. To tackle this problem, in this paper, we propose a Lightweight Dense Connected Approach with Attention for Single Image Super-Resolution (LDCASR), which employs the attention mechanism to extract useful information in channel dimension. Particularly, we propose the recursive dense group (RDG), consisting of Dense Attention Blocks (DABs), which can obtain more significant representations by extracting deep features with the aid of both dense connections and the attention module, making our whole network attach importance to learning more advanced feature information. Additionally, we introduce the group convolution in DABs, which can reduce the number of parameters to 0.6 M. Extensive experiments on benchmark datasets demonstrate the superiority of our proposed method over five chosen SISR methods.

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Bonini, Bianca F., Mauro Comes Franchini, Germana Mazzanti, Jan-Willem Slief, Margreth A. Wegman, and Binne Zwanenburg. "Sulfoxide induced sigmatropic rearrangement (SISR) of methyl 1-methylsulfanylvinyl sulfoxides." Chemical Communications, no. 11 (1997): 1011–12. http://dx.doi.org/10.1039/a701942d.

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Yang, Cheng, and Guanming Lu. "Deeply Recursive Low- and High-Frequency Fusing Networks for Single Image Super-Resolution." Sensors 20, no. 24 (December 18, 2020): 7268. http://dx.doi.org/10.3390/s20247268.

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With the development of researches on single image super-resolution (SISR) based on convolutional neural networks (CNN), the quality of recovered images has been remarkably promoted. Since then, many deep learning-based models have been proposed, which have outperformed the traditional SISR algorithms. According to the results of extensive experiments, the feature representations of the model can be enhanced by increasing the depth and width of the network, which can ultimately improve the image reconstruction quality. However, a larger network generally consumes more computational and memory resources, making it difficult to train the network and increasing the prediction time. In view of the above problems, a novel deeply-recursive low- and high-frequency fusing network (DRFFN) for SISR tasks is proposed in this paper, which adopts the structure of parallel branches to extract the low- and high-frequency information of the image, respectively. The different complexities of the branches can reflect the frequency characteristic of the diverse image information. Moreover, an effective channel-wise attention mechanism based on variance (VCA) is designed to make the information distribution of each feature map more reasonably with different variances. Owing to model structure (i.e., cascading recursive learning of recursive units), DRFFN and DRFFN-L are very compact, where the weights are shared by all convolutional recursions. Comprehensive benchmark evaluations in standard benchmark datasets well demonstrate that DRFFN outperforms the most existing models and has achieved competitive, quantitative, and visual results.

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Jia, Wei, Yang Zhao, Ronggang Wang, Shujie Li, Hai Min, and Xiaoping Liu. "Are Recent SISR Techniques Suitable for Industrial Applications at Low Magnification?" IEEE Transactions on Industrial Electronics 66, no. 12 (December 2019): 9828–36. http://dx.doi.org/10.1109/tie.2018.2886792.

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Xu, Wang, Renwen Chen, Bin Huang, Xiang Zhang, and Chuan Liu. "Single Image Super-Resolution Based on Global Dense Feature Fusion Convolutional Network." Sensors 19, no. 2 (January 14, 2019): 316. http://dx.doi.org/10.3390/s19020316.

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Deep neural networks (DNNs) have been widely adopted in single image super-resolution (SISR) recently with great success. As a network goes deeper, intermediate features become hierarchical. However, most SISR methods based on DNNs do not make full use of the hierarchical features. The features cannot be read directly by the subsequent layers, therefore, the previous hierarchical information has little influence on the subsequent layer output, and the performance is relatively poor. To address this issue, a novel global dense feature fusion convolutional network (DFFNet) is proposed, which can take full advantage of global intermediate features. Especially, a feature fusion block (FFblock) is introduced as the basic module. Each block can directly read raw global features from previous ones and then learns the feature spatial correlation and channel correlation between features in a holistic way, leading to a continuous global information memory mechanism. Experiments on the benchmark tests show that the proposed method DFFNet achieves favorable performance against the state-of-art methods.

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He, Ruiqiang, Xiangchu Feng, Chenping Zhao, Huazhu Chen, Xiaolong Zhu, and Chen Xu. "Image Restoration Based on Adaptive Dual-Domain Filtering." Mathematical Problems in Engineering 2018 (October 10, 2018): 1–17. http://dx.doi.org/10.1155/2018/4790174.

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Image restoration is a long-standing problem in low-level computer vision. In this paper, we offer a simple but effective estimation paradigm for various image restoration problems. Specifically, we first propose a model-based Gaussian denoising method Adaptive Dual-Domain Filtering (ADDF) by learning the optimal confidence factors which are adjusted adaptively with Gaussian noise standard deviation. In addition, by generalizing this learning approach to Laplace noise, the learning algorithm of the optimum confidence factors in Laplace denoising is presented. Finally, the proposed ADDF is tactfully plugged into the method frameworks of off-the-shelf image deblurring and single image super-resolution (SISR). The approach, coining the name Plug-ADDF, achieves promising performance. Extensive experiments validate that the proposed ADDF for Gaussian and Laplace noise removals indeed results in visual and quantitative improvements over some existing state-of-the-art methods. Moreover, our Plug-ADDF for image deblurring and SISR also demonstrates superior performance objectively and subjectively.

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Xu, Yingying, Jianhua Li, Haifeng Song, and Lei Du. "Single-Image Super-Resolution Using Panchromatic Gradient Prior and Variational Model." Mathematical Problems in Engineering 2021 (May 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/9944385.

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Single-image super-resolution (SISR) is a resolution enhancement technique and is known as an ill-posed problem. Motivated by the idea of pan-sharping, we propose a novel variational model for SISR. The structure tensor of the input low-resolution image is exploited to obtain the gradient of an imaginary panchromatic image. Then, by constraining the gradient consistency, the image edges and details can be better recovered during the procedure of restoration of high-resolution images. Besides, we resort to the nonlocal sparse and low-rank regularization of image patches to further improve the super-resolution performance. The proposed variational model is efficiently solved by ADMM-based algorithm. We do extensive experiments in natural images and remote sensing images with different magnifying factors and compare our method with three classical super-resolution methods. The subjective visual impression and quantitative evaluation indexes both show that our method can obtain higher-quality results.

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Muhammad, Wazir, and Supavadee Aramvith. "Multi-Scale Inception Based Super-Resolution Using Deep Learning Approach." Electronics 8, no. 8 (August 13, 2019): 892. http://dx.doi.org/10.3390/electronics8080892.

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Single image super-resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) image. In order to address the SISR problem, recently, deep convolutional neural networks (CNNs) have achieved remarkable progress in terms of accuracy and efficiency. In this paper, an innovative technique, namely a multi-scale inception-based super-resolution (SR) using deep learning approach, or MSISRD, was proposed for fast and accurate reconstruction of SISR. The proposed network employs the deconvolution layer to upsample the LR image to the desired HR image. The proposed method is in contrast to existing approaches that use the interpolation techniques to upscale the LR image. Primarily, interpolation techniques are not designed for this purpose, which results in the creation of undesired noise in the model. Moreover, the existing methods mainly focus on the shallow network or stacking multiple layers in the model with the aim of creating a deeper network architecture. The technique based on the aforementioned design creates the vanishing gradients problem during the training and increases the computational cost of the model. Our proposed method does not use any hand-designed pre-processing steps, such as the bicubic interpolation technique. Furthermore, an asymmetric convolution block is employed to reduce the number of parameters, in addition to the inception block adopted from GoogLeNet, to reconstruct the multiscale information. Experimental results demonstrate that the proposed model exhibits an enhanced performance compared to twelve state-of-the-art methods in terms of the average peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) with a reduced number of parameters for the scale factor of 2 × , 4 × , and 8 × .

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Schmied, G. "Conference of the SISR (previously CISR) in August 1991 in Maynooth, Ireland." Journal of Empirical Theology 5, no. 1 (1992): 96. http://dx.doi.org/10.1163/157092592x00082.

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Schmied, G. "Conference of the SISR (previously CISR) in August 1991 in Maynooth, Ireland." Journal of Empirical Theology 5, no. 2 (1992): 96. http://dx.doi.org/10.1163/157092592x00190.

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BONINI, B. F., M. C. FRANCHINI, G. MAZZANTI, J. W. SLIEF, M. A. WEGMAN, and B. ZWANENBURG. "ChemInform Abstract: Sulfoxide Induced Sigmatropic Rearrangement (SISR) of Methyl 1- Methylsulfanylvinyl Sulfoxides." ChemInform 28, no. 41 (August 3, 2010): no. http://dx.doi.org/10.1002/chin.199741105.

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Lin, Lujun, Yiming Fang, Xiaochen Du, and Zhu Zhou. "Generating High-Quality Air-Coupled Ultrasonic Images for Wooden Material Characterization by Single Image Super-Resolution." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 03 (July 10, 2019): 2054008. http://dx.doi.org/10.1142/s0218001420540087.

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As the practical applications in other fields, high-resolution images are usually expected to provide a more accurate assessment for the air-coupled ultrasonic (ACU) characterization of wooden materials. This paper investigated the feasibility of applying single image super-resolution (SISR) methods to recover high-quality ACU images from the raw observations that were constructed directly by the on-the-shelf ACU scanners. Four state-of-the-art SISR methods were applied to the low-resolution ACU images of wood products. The reconstructed images were evaluated by visual assessment and objective image quality metrics, including peak signal-to-noise-ratio and structural similarity. Both qualitative and quantitative evaluations indicated that the substantial improvement of image quality can be yielded. The results of the experiments demonstrated the superior performance and high reproducibility of the method for generating high-quality ACU images. Sparse coding based super-resolution and super-resolution convolutional neural network (SRCNN) significantly outperformed other algorithms. SRCNN has the potential to act as an effective tool to generate higher resolution ACU images due to its flexibility.

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Yu, Qiang, Feiqiang Liu, Long Xiao, Zitao Liu, and Xiaomin Yang. "Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network." International Journal of Environmental Research and Public Health 18, no. 11 (May 31, 2021): 5890. http://dx.doi.org/10.3390/ijerph18115890.

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Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

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Stolz, Jörg. "Secularization research and its competitors: A response to my critics." Social Compass 67, no. 2 (May 25, 2020): 337–46. http://dx.doi.org/10.1177/0037768620917331.

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This article is a response to the articles published in this issue of Social Compass by François Gauthier, Tobias Müller, David Voas and Sarah Wilkens-Laflamme about the presidential address given by Jörg Stolz at the SISR Congress in July 2019 and titled Secularization theories in the twenty-first century: Ideas, evidence, and problems.

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di Redazione, Comitato. "The italian society of accounting history SISR 2020 webinar series. Call for papers." CONTABILITÀ E CULTURA AZIENDALE, no. 1 (September 2020): 61–63. http://dx.doi.org/10.3280/cca2020-001005.

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Wagner, L., L. Liebel, and M. Körner. "DEEP RESIDUAL LEARNING FOR SINGLE-IMAGE SUPER-RESOLUTION OF MULTI-SPECTRAL SATELLITE IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 189–96. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-189-2019.

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Abstract. Analyzing optical remote sensing imagery depends heavily on their spatial resolution. At the same time, this data is adversely affected by fixed sensor parameters and environmental influences. Methods for increasing the quality of such data and concomitantly optimizing its information content are, thus, in high demand. In particular, single-image super-resolution (SISR) approaches aim to achieve this goal solely by observing the individual images.We propose to adapt a generic deep residual neural network architecture for SISR to deal with the special properties of remote sensing satellite imagery, especially taking into account the different spatial resolutions of individual Sentinel-2 bands, i.e., ground sampling distances of 20&thinsp;m and 10&thinsp;m. As a result, this method is able to increase the perceived resolution of the 20&thinsp;m channels and mesh all spectral bands. Experimental evaluation and ablation studies on large datasets have shown superior performance compared to the state-of-the-art and that the model is not bound by its capacity.

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Zang, Huaijuan, Leilei Zhu, Zhenglong Ding, Xinke Li, and Shu Zhan. "Cascaded Dense-UNet for Image Super-Resolution." Journal of Circuits, Systems and Computers 29, no. 08 (October 11, 2019): 2050121. http://dx.doi.org/10.1142/s0218126620501212.

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Recently, deep convolutional neural networks (CNNs) have achieved great success in single image super-resolution (SISR). Especially, dense skip connections and residual learning structures promote better performance. While most existing deep CNN-based networks exploit the interpolation of upsampled original images, or do transposed convolution in the reconstruction stage, which do not fully employ the hierarchical features of the networks for final reconstruction. In this paper, we present a novel cascaded Dense-UNet (CDU) structure to take full advantage of all hierarchical features for SISR. In each Dense-UNet block (DUB), many short, dense skip pathways can facilitate the flow of information and integrate the different receptive fields. A series of DUBs are concatenated to acquire high-resolution features and capture complementary contextual information. Upsampling operators are in DUBs. Furthermore, residual learning is introduced to our network, which can fuse shallow features from low resolution (LR) image and deep features from cascaded DUBs to further boost super-resolution (SR) reconstruction results. The proposed method is evaluated quantitatively and qualitatively on four benchmark datasets, our network achieves comparable performance to state-of-the-art super-resolution approaches and obtains pleasant visualization results.

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Gu, Jun, Xian Sun, Yue Zhang, Kun Fu, and Lei Wang. "Deep Residual Squeeze and Excitation Network for Remote Sensing Image Super-Resolution." Remote Sensing 11, no. 15 (August 3, 2019): 1817. http://dx.doi.org/10.3390/rs11151817.

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Recently, deep convolutional neural networks (DCNN) have obtained promising results in single image super-resolution (SISR) of remote sensing images. Due to the high complexity of remote sensing image distribution, most of the existing methods are not good enough for remote sensing image super-resolution. Enhancing the representation ability of the network is one of the critical factors to improve remote sensing image super-resolution performance. To address this problem, we propose a new SISR algorithm called a Deep Residual Squeeze and Excitation Network (DRSEN). Specifically, we propose a residual squeeze and excitation block (RSEB) as a building block in DRSEN. The RSEB fuses the input and its internal features of current block, and models the interdependencies and relationships between channels to enhance the representation power. At the same time, we improve the up-sampling module and the global residual pathway in the network to reduce the parameters of the network. Experiments on two public remote sensing datasets (UC Merced and NWPU-RESISC45) show that our DRSEN achieves better accuracy and visual improvements against most state-of-the-art methods. The DRSEN is beneficial for the progress in the remote sensing images super-resolution field.

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Ma, Wen, Zongxu Pan, Feng Yuan, and Bin Lei. "Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network." Remote Sensing 11, no. 21 (November 3, 2019): 2578. http://dx.doi.org/10.3390/rs11212578.

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Single image super-resolution (SISR) has been widely studied in recent years as a crucial technique for remote sensing applications. In this paper, a dense residual generative adversarial network (DRGAN)-based SISR method is proposed to promote the resolution of remote sensing images. Different from previous super-resolution (SR) approaches based on generative adversarial networks (GANs), the novelty of our method mainly lies in the following factors. First, we made a breakthrough in terms of network architecture to improve performance. We designed a dense residual network as the generative network in GAN, which can make full use of the hierarchical features from low-resolution (LR) images. We also introduced a contiguous memory mechanism into the network to take advantage of the dense residual block. Second, we modified the loss function and altered the model of the discriminative network according to the Wasserstein GAN with a gradient penalty (WGAN-GP) for stable training. Extensive experiments were performed using the NWPU-RESISC45 dataset, and the results demonstrated that the proposed method outperforms state-of-the-art methods in terms of both objective evaluation and subjective perspective.

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Li, Jianhong, Yarong Wu, Xiaonan Luo, Chengcai Leng, and Bo Li. "Single Image Superresolution Using Maximizing Self-Similarity Prior." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/312423.

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Single image superresolution (SISR) requires only one low-resolution (LR) image as its input which thus strongly motivates researchers to improve the technology. The property that small image patches tend to recur themselves across different scales is very important and widely used in image processing and computer vision community. In this paper, we develop a new approach for solving the problem of SISR by generalizing this property. The main idea of our approach takes advantage of a generic prior that assumes that a randomly selected patch in the underlying high-resolution (HR) image should visually resemble as much as possible with some patch extracted from the input low-resolution (LR) image. Observing the proposed prior, our approach deploys a cost function and applies an iterative scheme to estimate the optimal HR image. For solving the cost function, we introduce Gaussian mixture model (GMM) to train on a sampled data set for approximating the joint probability density function (PDF) of input image with different scales. Through extensive comparative experiments, this paper demonstrates that the visual fidelity of our proposed method is often superior to those generated by other state-of-the-art algorithms as determined through both perceptual judgment and quantitative measures.

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Fu, Chunmei, and Yong Yin. "Edge-Enhanced with Feedback Attention Network for Image Super-Resolution." Sensors 21, no. 6 (March 15, 2021): 2064. http://dx.doi.org/10.3390/s21062064.

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Significant progress has been made in single image super-resolution (SISR) based on deep convolutional neural networks (CNNs). The attention mechanism can capture important features well, and the feedback mechanism can realize the fine-tuning of the output to the input. However, they have not been reasonably applied in the existing deep learning-based SISR methods. Additionally, the results of the existing methods still have serious artifacts and edge blurring. To address these issues, we proposed an Edge-enhanced with Feedback Attention Network for image super-resolution (EFANSR), which comprises three parts. The first part is an SR reconstruction network, which adaptively learns the features of different inputs by integrating channel attention and spatial attention blocks to achieve full utilization of the features. We also introduced feedback mechanism to feed high-level information back to the input and fine-tune the input in the dense spatial and channel attention block. The second part is the edge enhancement network, which obtains a sharp edge through adaptive edge enhancement processing on the output of the first SR network. The final part merges the outputs of the first two parts to obtain the final edge-enhanced SR image. Experimental results show that our method achieves performance comparable to the state-of-the-art methods with lower complexity.

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Chadha, Aman, John Britto, and M. Mani Roja. "iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks." Computational Visual Media 6, no. 3 (July 20, 2020): 307–17. http://dx.doi.org/10.1007/s41095-020-0175-7.

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Abstract Recently, learning-based models have enhanced the performance of single-image super-resolution (SISR). However, applying SISR successively to each video frame leads to a lack of temporal coherency. Convolutional neural networks (CNNs) outperform traditional approaches in terms of image quality metrics such as peak signal to noise ratio (PSNR) and structural similarity (SSIM). On the other hand, generative adversarial networks (GANs) offer a competitive advantage by being able to mitigate the issue of a lack of finer texture details, usually seen with CNNs when super-resolving at large upscaling factors. We present iSeeBetter, a novel GAN-based spatio-temporal approach to video super-resolution (VSR) that renders temporally consistent super-resolution videos. iSeeBetter extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its generator. Furthermore, to improve the “naturality” of the super-resolved output while eliminating artifacts seen with traditional algorithms, we utilize the discriminator from super-resolution generative adversarial network. Although mean squared error (MSE) as a primary loss-minimization objective improves PSNR/SSIM, these metrics may not capture fine details in the image resulting in misrepresentation of perceptual quality. To address this, we use a four-fold (MSE, perceptual, adversarial, and total-variation loss function. Our results demonstrate that iSeeBetter offers superior VSR fidelity and surpasses state-of-the-art performance.

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Wei, Zikang, and Yunqing Liu. "Deep Intelligent Neural Network for Medical Geographic Small-target Intelligent Satellite Image Super-resolution." Journal of Imaging Science and Technology 65, no. 3 (May 1, 2021): 30406–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2021.65.3.030406.

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Abstract In the field of single-image super-resolution (SISR) research, neural networks and deep learning methods are gradually being widely used by researchers. Over time, the fields of application have expanded in scope. The SISR method is also applied in the field of intelligent satellite imagery. In recent years, research applications based on intelligent satellite images have mostly focused on imaging, classification, and segmentation. They have rarely been used in actual observation problems. This article proposes a new intelligent neural network model, the Laplacian pyramid residual dense network, for the super-resolution of hyperspectral satellite medical geographic small-targets. This study proceeds in three steps. First, the three-layer Laplacian pyramid structure is designed to increase the depth of the image at the feature extraction stage. Second, the residual mode is improved and updated; a new residual block is proposed for constructing the residual dense network to enhance the feature details of the image during the training process. In the third step, an end-to-end network is established directly through the residual structure for eliminating unnecessary visualization during the process and for ease of training. According to the experimental results, it has been proved that the deep intelligent neural network method proposed here has achieved good results in the application for super-resolution of medical geographic small-target intelligent satellite images.

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Widyastuti, Susana. "KOREKSI TUTURAN OLEH SBY (STUDI KASUS WAWANCARA KHUSUS “SAATNYA SBY BICARA” DI METRO TV)." Lingua Didaktika: Jurnal Bahasa dan Pembelajaran Bahasa 3, no. 2 (July 12, 2010): 161. http://dx.doi.org/10.24036/ld.v3i2.7377.

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This research aims at describing the reasons, mechanism and forms of self-repair as well as describing the relationship between the employment of self-repair and SBY as the speaker. Data of the research were SBY’s utterances in an exclusive interview called “Saatnya SBY Bicara” broadcasted by Metro TV. Data were transcribed and analyzed carefully using qualitative method as the main method and quantitative method as the supporting method to provide frequency of data occurance. The research findings show that there are two main reasons of the employment of self-repair by SBY, they are the problems of speaking and understanding. Two mechanism of self-repair found are self-initiated self-repair (SISR) and other-initiated self-repair (OISR). In the mechanism of SISR, SBY employs three forms of repair (R), they are word search, word/phrase replacing, and explanation. These three forms of repair are preceded by two forms of repair-initiation (RI), they are lexical markers and non-lexical markers. In the mechanism of OISR, SBY employs four forms of repair (R), they are accepting, rejecting, explaining and limiting statement. These four forms of repair are preceded by three forms of repair-initiation (RI), they are clarification, non-understanding, and interpretation. The employment of self-repair reflects SBY as the speaker, such as SBY as a competent and careful person, and SBY as a polite person who tries to maintain good relationship with others and not to hurt others’ feeling.

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Kesrarat, Darun, Kornkamol Thakulsukanant, and Vorapoj Patanavijit. "A novel elementary spatial expanding scheme form on SISR method with modifying geman & mcclure function." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 5 (October 1, 2019): 2554. http://dx.doi.org/10.12928/telkomnika.v17i5.12799.

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Mu, Bin, Bo Qin, Shijin Yuan, and Xiaoyun Qin. "A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events." Mathematical Problems in Engineering 2020 (November 15, 2020): 1–17. http://dx.doi.org/10.1155/2020/7897824.

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Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research. The two main approaches for climate downscaling are dynamical and statistical. The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs). Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV). This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness. Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena. However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details. In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling. More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events. Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN). It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions. We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID). The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe. In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models.

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Muhammad, Wazir, Zuhaibuddin Bhutto, Arslan Ansari, Mudasar Latif Memon, Ramesh Kumar, Ayaz Hussain, Syed Ali Raza Shah, Imdadullah Thaheem, and Shamshad Ali. "Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution." Electronics 10, no. 16 (August 17, 2021): 1979. http://dx.doi.org/10.3390/electronics10161979.

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Recent research on single-image super-resolution (SISR) using deep convolutional neural networks has made a breakthrough and achieved tremendous performance. Despite their significant progress, numerous convolutional neural networks (CNN) are limited in practical applications, owing to the requirement of the heavy computational cost of the model. This paper proposes a multi-path network for SISR, known as multi-path deep CNN with residual inception network for single image super-resolution. In detail, a residual/ResNet block with an Inception block supports the main framework of the entire network architecture. In addition, remove the batch normalization layer from the residual network (ResNet) block and max-pooling layer from the Inception block to further reduce the number of parameters to preventing the over-fitting problem during the training. Moreover, a conventional rectified linear unit (ReLU) is replaced with Leaky ReLU activation function to speed up the training process. Specifically, we propose a novel two upscale module, which adopts three paths to upscale the features by jointly using deconvolution and upsampling layers, instead of using single deconvolution layer or upsampling layer alone. The extensive experimental results on image super-resolution (SR) using five publicly available test datasets, which show that the proposed model not only attains the higher score of peak signal-to-noise ratio/structural similarity index matrix (PSNR/SSIM) but also enables faster and more efficient calculations against the existing image SR methods. For instance, we improved our method in terms of overall PSNR on the SET5 dataset with challenging upscale factor 8× as 1.88 dB over the baseline bicubic method and reduced computational cost in terms of number of parameters 62% by deeply-recursive convolutional neural network (DRCN) method.

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Fontaine, Louise. "L’encadrement étatique de la croyance religieuse dans la pensée de Jacques Zylberberg." Social Compass 64, no. 2 (May 25, 2017): 233–46. http://dx.doi.org/10.1177/0037768617697916.

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Résumé Cet article explore l’approche et les concepts développés par Jacques Zylberberg dans le domaine de la sociologie des religions. Trois groupes religieux retiennent tout particulièrement l’attention ici : les Témoins de Jéhovah, les Hassidim et les Pentecôtistes catholiques (ou Charismatiques dans le cas du Québec). Le but poursuivi est de préciser en quoi Zylberberg a joué un rôle significatif pour la revue Social Compass et aussi auprès de la Société Internationale de Sociologie des Religions (SISR) tout au long de sa carrière universitaire. Cette réflexion cherche aussi à faire ressortir comment Zylberberg a procédé pour articuler des dimensions théoriques et empiriques face à ce qu’il désigne comme étant l’organisation étatique du religieux.

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Holmes, David R., Paul S. Teirstein, Lowell Satler, Michael H. Sketch, Jeffrey J. Popma, Laura Mauri, Hong P. Wang, and Patricia A. Schleckser. "5-YEAR FINAL RESULTS OF THE SISR (SIROLIMUS-ELUTING STENTS VERSUS VASCULAR BRACHYTHERAPY FOR IN-STENT RESTENOSIS) TRIAL." Journal of the American College of Cardiology 57, no. 14 (April 2011): E1641. http://dx.doi.org/10.1016/s0735-1097(11)61641-0.

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Holmes, David R., Paul S. Teirstein, Lowell Satler, Michael H. Sketch, Jeffery J. Popma, Laura Mauri, Hong (Patrick) Wang, Patricia A. Schleckser, and Sidney A. Cohen. "3-Year Follow-Up of the SISR (Sirolimus-Eluting Stents Versus Vascular Brachytherapy for In-Stent Restenosis) Trial." JACC: Cardiovascular Interventions 1, no. 4 (August 2008): 439–48. http://dx.doi.org/10.1016/j.jcin.2008.05.010.

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Alli, Oluseun O., Paul S. Teirstein, Lowell Satler, Michael H. Sketch, Jeffery J. Popma, Laura Mauri, Hong (Patrick) Wang, Patricia A. Schleckser, Sidney A. Cohen, and David R. Holmes. "Five-year follow-up of the Sirolimus-Eluting Stents vs Vascular Brachytherapy for Bare Metal In-Stent Restenosis (SISR) trial." American Heart Journal 163, no. 3 (March 2012): 438–45. http://dx.doi.org/10.1016/j.ahj.2011.11.019.

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Sahito, Faisal, Pan Zhiwen, Junaid Ahmed, and Raheel Ahmed Memon. "Wavelet-Integrated Deep Networks for Single Image Super-Resolution." Electronics 8, no. 5 (May 17, 2019): 553. http://dx.doi.org/10.3390/electronics8050553.

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We propose a scale-invariant deep neural network model based on wavelets for single image super-resolution (SISR). The wavelet approximation images and their corresponding wavelet sub-bands across all predefined scale factors are combined to form a big training data set. Then, mappings are determined between the wavelet sub-band images and their corresponding approximation images. Finally, the gradient clipping process is used to boost the training speed of the algorithm. Furthermore, stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT), due to its up-scaling property. In this way, we can preserve more information about the images. In the proposed model, the high-resolution image is recovered with detailed features, due to redundancy (across the scale) property of wavelets. Experimental results show that the proposed model outperforms state-of-the algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

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Copic, Vedrana, Frank P. Deane, Trevor P. Crowe, and Lindsay G. Oades. "Hope, Meaning and Responsibility across Stages of Recovery for Individuals Living With an Enduring Mental Illness." Australian Journal of Rehabilitation Counselling 17, no. 2 (December 1, 2011): 61–73. http://dx.doi.org/10.1375/jrc.17.2.61.

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AbstractThis study reports on the relationship between stage of recovery and hope, meaning and responsibility for individuals diagnosed with severe mental illness.Methods:Seventy-seven people with a diagnosis of a psychotic disorder of at least 6 months' duration participated in the study. Participants completed the Self-Identified Stage of Recovery (SISR) scale, measures of component processes of recovery (Hope Scale; Positive Interpretation of Disease, SpREUK; Active Involvement, Personal Health Management Questionnaire (PHMQ) and the Recovery Assessment Scale-short (RAS).Results:Hope, meaning,Personal Confidence and HopeandNot Being Dominated by Symptomsvaried significantly across stages of recovery; however, neither in a parallel nor linear fashion. Hopefulness and sense of meaning in relation to the experience of mental illness increase before personal confidence and resilience in the face of setbacks.Conclusions and implications:Symptoms appear to take less prominence in individuals' lives in later stages of recovery. Greater insight into the relationship between stage of recovery and component processes may allow for more targeted recovery-oriented support for individuals at different stages of recovery.

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Suryanarayana, Gunnam, Ravindra Dhuli, and Jie Yang. "Single Image Super-Resolution Algorithm Possessing Edge and Contrast Preservation." International Journal of Image and Graphics 19, no. 04 (October 2019): 1950024. http://dx.doi.org/10.1142/s0219467819500244.

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In real time surveillance video applications, it is often required to identify a region of interest in a degraded low resolution (LR) image. State-of-the-art super-resolution (SR) techniques produce images with poor illumination and degraded high frequency details. In this paper, we present a different approach for SISR by correcting the dual-tree complex wavelet transform (DT-CWT) subbands using the multi-stage cascaded joint bilateral filter (MSCJBF) and singular value decomposition (SVD). The proposed method exploits geometric regularity for implementing the covariance-based interpolation in the spatial domain. We decompose the interpolated LR image into different image and wavelet coefficients by employing DT-CWT. To preserve edges, we alter the wavelet sub-bands with the high frequency details obtained from the MSCJBF. Simultaneously, we retain uniform illumination by improving the image coefficients using SVD. In addition, the wavelet sub-bands undergo lanczos interpolation prior to the subband refinement. Experimental results demonstrate the effectiveness of our method.

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Marsi, Stefano, Jhilik Bhattacharya, Romina Molina, and Giovanni Ramponi. "A Non-Linear Convolution Network for Image Processing." Electronics 10, no. 2 (January 17, 2021): 201. http://dx.doi.org/10.3390/electronics10020201.

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This paper proposes a new neural network structure for image processing whose convolutional layers, instead of using kernels with fixed coefficients, use space-variant coefficients. The adoption of this strategy allows the system to adapt its behavior according to the spatial characteristics of the input data. This type of layers performs, as we demonstrate, a non-linear transfer function. The features generated by these layers, compared to the ones generated by canonical CNN layers, are more complex and more suitable to fit to the local characteristics of the images. Networks composed by these non-linear layers offer performance comparable with or superior to the ones which use canonical Convolutional Networks, using fewer layers and a significantly lower number of features. Several applications of these newly conceived networks to classical image-processing problems are analyzed. In particular, we consider: Single-Image Super-Resolution (SISR), Edge-Preserving Smoothing (EPS), Noise Removal (NR), and JPEG artifacts removal (JAR).

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Lacroix, Y., M. F. Vachon, and M. Champagne. "#307 Use of selective inhibitors of serotonineʼs recaptation (SISR) for pediatric patients presenting symptoms of acute traumatic stress before bone marrow transplantation." Journal of Pediatric Hematology/Oncology 21, no. 4 (July 1999): 316. http://dx.doi.org/10.1097/00043426-199907000-00049.

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Cinquini, Lino, Alessandro Marelli, and Andrea Tenucci. "AN ANALYSIS OF PUBLISHING PATTERNS IN ACCOUNTING HISTORY RESEARCH IN ITALY, 1990–2004." Accounting Historians Journal 35, no. 1 (June 1, 2008): 1–48. http://dx.doi.org/10.2308/0148-4184.35.1.1.

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In the last decade, an increasing number of analyses of accounting history literature have been undertaken to classify historical research paths and to “map” the variety of approaches and issues of the discipline in different geographical settings so as to make international comparisons. The paper develops these topics in the Italian context by studying the development of accounting history research (AHR) in the last 15 years. Contributions by Italian authors have been published in international and national specialist journals as well as in more general accounting journals. Other papers have been presented and published in the proceedings of the biannual SISR (Società Italiana di Storia della Ragioneria) Congress and in the Congress celebrating the 500th anniversary of the publication of Pacioli's Summa held in Venice in 1994. The findings chart publication trends during the period 1990–2004 from a quantitative and qualitative perspective, based on different dimensions, the dynamic of change in Italian AHR, and its possible limitations. The paper is informed by an international perspective and causal interpretations are attempted.

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Bhujel, Anil, and Dibakar Raj Pant. "Dynamic Convolutional Neural Network For Image Super-resolution." Journal of Advanced College of Engineering and Management 3 (January 10, 2018): 1. http://dx.doi.org/10.3126/jacem.v3i0.18808.

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Single image super-resolution (SISR) is a technique that reconstructs high resolution image from single low resolution image. Dynamic Convolutional Neural Network (DCNN) is used here for the reconstruction of high resolution image from single low resolution image. It takes low resolution image as input and produce high resolution image as output for dynamic up-scaling factor 2, 3, and 4. The dynamic convolutional neural network directly learns an end-to-end mapping between low resolution and high resolution images. The CNN trained simultaneously with images up-scaled by factors 2, 3, and 4 to make it dynamic. The system is then tested for the input images with up-scaling factors 2, 3 and 4. The dynamically trained CNN performs well for all three up-scaling factors. The performance of network is measured by PSNR, WPSNR, SSIM, MSSSIM, and also by perceptual.Journal of Advanced College of Engineering and Management, Vol. 3, 2017, Page: 1-10

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Journal articles on the topic 'SISR'

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