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Journal articles on the topic 'Low-light enhancement and denoising'

Author: Grafiati

Published: 6 September 2023

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1

Carré, Maxime, and Michel Jourlin. "Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising." Sensors 21, no. 23 (2021): 7906. http://dx.doi.org/10.3390/s21237906.

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Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the abi

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Zhang, Jialiang, Ruiwen Ji, Jingwen Wang, Hongcheng Sun, and Mingye Ju. "DEGAN: Decompose-Enhance-GAN Network for Simultaneous Low-Light Image Lightening and Denoising." Electronics 12, no. 14 (2023): 3038. http://dx.doi.org/10.3390/electronics12143038.

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Images taken in low-light situations frequently have a significant quality reduction. Taking care of these degradation problems in low-light images is essential for raising their visual quality and enhancing high-level visual task performance. However, because of the inherent information loss in dark images, conventional Retinex-based approaches for low-light image enhancement frequently fail to accomplish real denoising. This research introduces DEGANet, a revolutionary deep-learning framework created particularly for improving and denoising low-light images. To overcome these restrictions, D

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Kim, Minjae, Dubok Park, David Han, and Hanseok Ko. "A novel approach for denoising and enhancement of extremely low-light video." IEEE Transactions on Consumer Electronics 61, no. 1 (2015): 72–80. http://dx.doi.org/10.1109/tce.2015.7064113.

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Malik, Sameer, and Rajiv Soundararajan. "A low light natural image statistical model for joint contrast enhancement and denoising." Signal Processing: Image Communication 99 (November 2021): 116433. http://dx.doi.org/10.1016/j.image.2021.116433.

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Das Mou, Trisha, Saadia Binte Alam, Md Hasibur Rahman, Gautam Srivastava, Mahady Hasan, and Mohammad Faisal Uddin. "Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation." Applied Sciences 13, no. 2 (2023): 1034. http://dx.doi.org/10.3390/app13021034.

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Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various

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Han, Guang, Yingfan Wang, Jixin Liu, and Fanyu Zeng. "Low-light images enhancement and denoising network based on unsupervised learning multi-stream feature modeling." Journal of Visual Communication and Image Representation 96 (October 2023): 103932. http://dx.doi.org/10.1016/j.jvcir.2023.103932.

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Hu, Linshu, Mengjiao Qin, Feng Zhang, Zhenhong Du, and Renyi Liu. "RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images." Remote Sensing 13, no. 1 (2020): 62. http://dx.doi.org/10.3390/rs13010062.

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Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-

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Zhao, Meng Ling, and Min Xia Jiang. "Research on Enhanced of Mine-Underground Picture." Advanced Materials Research 490-495 (March 2012): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.548.

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Because of the based on S3C6410 Field information recorder mine- underground non-uniform illumination and mine- underground non-uniform illumination that a large of noise collected and transferred,image is low contrast ,dim and dark. Based on the theory of Donoho's wavelet threshold denoising, several typical wavelet threshold denoising methods are compared.the best denoising effect of peak signal to noise ratio is obtained. The image enhancement method that combination of the adaptive thresholding denoising and histogram equalization is proposed. The experiment result shows that the method ha

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Wu, Zeju, Yang Ji, Lijun Song, and Jianyuan Sun. "Underwater Image Enhancement Based on Color Correction and Detail Enhancement." Journal of Marine Science and Engineering 10, no. 10 (2022): 1513. http://dx.doi.org/10.3390/jmse10101513.

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To solve the problems of underwater image color deviation, low contrast, and blurred details, an algorithm based on color correction and detail enhancement is proposed. First, the improved nonlocal means denoising algorithm is used to denoise the underwater image. The combination of Gaussian weighted spatial distance and Gaussian weighted Euclidean distance is used as the index of nonlocal means denoising algorithm to measure the similarity of structural blocks. The improved algorithm can retain more edge features and texture information while maintaining noise reduction ability. Then, the imp

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Xu, Xiaogang, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. "Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3054–62. http://dx.doi.org/10.1609/aaai.v37i3.25409.

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Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency between the models. This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR effi

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Tian, Mingchuan, and Jizheng Liu. "Low-Power Communication Signal Enhancement Method of Internet of Things Based on Nonlocal Mean Denoising." Wireless Communications and Mobile Computing 2022 (July 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/5167639.

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In order to improve the transmission effect of low-power communication signal of Internet of Things and compress the enhancement time of low-power communication signal, this paper designs a low-power communication signal enhancement method of Internet of Things based on nonlocal mean denoising. Firstly, the residual of one-dimensional communication layer is preprocessed by convolution core to obtain the residual of one-dimensional communication layer. Then, according to the two classification recognition methods, the noise reduction signal feature recognition of the low-power communication sig

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Hassan, Raaid N. "A comparison between PCA and some enhancement filters for denoising astronomical images." Iraqi Journal of Physics (IJP) 11, no. 22 (2019): 82–92. http://dx.doi.org/10.30723/ijp.v11i22.356.

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This paper includes a comparison between denoising techniques by using statistical approach, principal component analysis with local pixel grouping (PCA-LPG), this procedure is iterated second time to further improve the denoising performance, and other enhancement filters were used. Like adaptive Wiener low pass-filter to a grayscale image that has been degraded by constant power additive noise, based on statistics estimated from a local neighborhood of each pixel. Performs Median filter of the input noisy image, each output pixel contains the Median value in the M-by-N neighborhood around th

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Sun, Qingjiao, Huiyan Jiang, Ganzheng Zhu, et al. "HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7478219.

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Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinui

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Yu, Lijia, Jie Luo, Shaoping Xu, Xiaojun Chen, and Nan Xiao. "An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers." Applied Sciences 12, no. 12 (2022): 6227. http://dx.doi.org/10.3390/app12126227.

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Image denoising is a classic but still important issue in image processing as the denoising effect has a significant impact on subsequent image processing results, such as target recognition and edge detection. In the past few decades, various denoising methods have been proposed, such as model-based and learning-based methods, and they have achieved promising results. However, no stand-alone method consistently outperforms the others in different complex imaging situations. Based on the complementary strengths of model-based and learning-based methods, in this study, we design a pixel-level i

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Li, Shunlei, Muhammad Adeel Azam, Ajay Gunalan, and Leonardo S. Mattos. "One-Step Enhancer: Deblurring and Denoising of OCT Images." Applied Sciences 12, no. 19 (2022): 10092. http://dx.doi.org/10.3390/app121910092.

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Optical coherence tomography (OCT) is a rapidly evolving imaging technology that combines a broadband and low-coherence light source with interferometry and signal processing to produce high-resolution images of living tissues. However, the speckle noise introduced by the low-coherence interferometry and the blur from device motions significantly degrade the quality of OCT images. Convolutional neural networks (CNNs) are a potential solution to deal with these issues and enhance OCT image quality. However, training such networks based on traditional supervised learning methods is impractical d

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Jiang, Hui Qin, Zhong Yong Wang, Ling Ma, Yu Min Liu, and Ping Li. "Wavelet-Based Medical Image Denoising and Enhancement." Applied Mechanics and Materials 195-196 (August 2012): 515–20. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.515.

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The visual quality of medical images is an important aspect in PACS implementation. In this study, on the basis of wavelet analysis, a denoising and enhancement algorithm for medical image is proposed. The algorithm mainly includes six steps. At first, an effcient method is investigated for Poisson Noise remove. Second, diagnosis features of the denoised image are enhanced by compressing the dynamic range. Third, we extract the high frequency component of the original image by the designed lowpass filter. Fourth, the extracted high frequency component are segment into diagnosis feature compone

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Cutmore, Tim R. H., and Patrick Celka. "Composite Noise Reduction of ERPs Using Wavelet, Model-Based, and Principal Component Subspace Methods." Journal of Psychophysiology 22, no. 3 (2008): 111–20. http://dx.doi.org/10.1027/0269-8803.22.3.111.

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This paper used three theoretically different algorithms for reducing noise in event-related potential (ERP) data. It examined the possibility that a hybrid of these methods could show gains in noise reduction beyond that obtained with any single method. The well-known ERP oddball paradigm was used to evaluate three denoising methods: statistical wavelet transform (wavelet-Z), a smooth subspace wavelet filter (wavelet-S), and subspace PCA. The six possible orders of serial application of these methods to the oddball waveforms were compared for efficacy in signal enhancement. It was found that

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P, Karuppusamy. "TECHNIQUES FOR ENHANCEMENT AND DENOISING OF UNDERWATER IMAGES: A REVIEW." Journal of Innovative Image Processing 1, no. 02 (2019): 81–90. http://dx.doi.org/10.36548/jiip.2019.2.003.

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The images observed from the underwater are usually of low quality because of the scattering of lights, ripples in water and the organic matters resolved in the water. So the preprocessing becomes an important necessity for the images obtained from under water before subjected to the future operations. The various degree of distortions suffered from by the underwater images could be preprocessed by applying the denoising and the image enhancement techniques. The Review addressing the techniques available in enhancing and denoising the underwater images is presented in the paper.

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Wang, Xuan, Liju Yin, Mingliang Gao, Zhenzhou Wang, Jin Shen, and Guofeng Zou. "Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform." Sensors 19, no. 11 (2019): 2462. http://dx.doi.org/10.3390/s19112462.

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Multi-pixel photon counting detectors can produce images in low-light environments based on passive photon counting technology. However, the resulting images suffer from problems such as low contrast, low brightness, and some unknown noise distribution. To achieve a better visual effect, this paper describes a denoising and enhancement method based on a block-matching 3D filter and a non-subsampled contourlet transform (NSCT). First, the NSCT was applied to the original image and histogram-equalized image to obtain the sub-band low- and high-frequency coefficients. Regional energy and scale co

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Zhou, Daxin, Yurong Qian, Yuanyuan Ma, Yingying Fan, Jianeng Yang, and Fuxiang Tan. "Low illumination image enhancement based on multi-scale CycleGAN with deep residual shrinkage." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 2383–95. http://dx.doi.org/10.3233/jifs-211664.

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Low-illumination image restoration has been widely used in many fields. Aiming at the problem of low resolution and noise amplification in low light environment, this paper applies style transfer of CycleGAN(Cycle-Consistent Generative Adversarial Networks) to low illumination image enhancement. In the design network structure, different convolution kernels are used to extract the features from three paths, and the deep residual shrinkage network is designed to suppress the noise after convolution. The color deviation of the image can be resolved by the identity loss of CycleGAN. In the discri

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Suganthy, M., S. Lakshmi, and S. Palanivel. "Enhancing the Quality of Underwater Images using Fusion of sequential Filters and Dehazing." International Journal of Engineering & Technology 7, no. 2.24 (2018): 296. http://dx.doi.org/10.14419/ijet.v7i2.24.12067.

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Effectively analyzing underwater images and identifying any object under the water has become a difficult task. Generally, the factors affecting underwater images are uneven lighting, low contrast, blunt colors, and characteristics of an object based on absorption and scattering of light. The proposed technique involves applying white balancing and contrast enhancement to the original image. The combination of filters namely homomorphic filtering, wavelet denoising, bilateral filter , adaptive filters are used and applied sequentially on the degraded underwater images. The results obtained sho

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Chen, Xiaohua, Qiang Sheng, and Bhupesh Kumar Singh. "Aerobics Image Classification Algorithm Based on Modal Symmetry Algorithm." Computational Intelligence and Neuroscience 2021 (September 3, 2021): 1–9. http://dx.doi.org/10.1155/2021/5970957.

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There exist large numbers of methods/algorithms which can be used for the classification of aerobic images. While the current method is used to classify the aerobics image, it cannot effectively remove the noise in the aerobics image. The classification time is long, and there are problems of poor denoising effect and low classification efficiency. Therefore, the aerobics image classification algorithm based on the modal symmetry algorithm is proposed. The method of nonlocal mean filtering based on structural features is used to denoise the aerobics image, and the pyramid structure of the imag

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Wang, Min, Zhen Li, Xiangjun Duan, and Wei Li. "An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/469350.

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This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs

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Ye, Jun, and Xian Zhang. "Hyperspectral Image Denoising via Subspace Low-rank Representation and Spatial‐spectral Total Variation." Journal of Imaging Science and Technology 64, no. 1 (2020): 10507–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.1.010507.

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Abstract Hyperspectral images (HSIs) acquired actually often contain various types of noise, such as Gaussian noise, impulse noise, and dead lines. On the basis of land covers, the spectral vectors in HSI can be separated into different classifications, which means the spectral space can be regarded as a union of several low-rank (LR) subspaces rather than a single LR subspace. Recently, LR constraint has been widely applied for denoising HSI. However, those LR-based methods do not constrain the intrinsic structure of spectral space. And these methods cannot make better use of the spatial or s

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Yang, Jun, Junyang Chen, Jun Li, Shijie Dai, and Yihui He. "An Improved Median Filter Based on YOLOv5 Applied to Electrochemiluminescence Image Denoising." Electronics 12, no. 7 (2023): 1544. http://dx.doi.org/10.3390/electronics12071544.

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In many experiments, the electrochemiluminescence images captured by smartphones often have a lot of noise, which makes it difficult for researchers to accurately analyze the light spot information from the captured images. Therefore, it is very important to remove the noise in the image. In this paper, a Center-Adaptive Median Filter (CAMF) based on YOLOv5 is proposed. Unlike other traditional filtering algorithms, CAMF can adjust its size in real-time according to the current pixel position, the center and the boundary frame of each light spot, and the distance between them. This gives CAMF

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Jeon, Yeong-Jae, Shin-Eui Park, Keun-A. Chang, and Hyeon-Man Baek. "Signal-to-Noise Ratio Enhancement of Single-Voxel In Vivo 31P and 1H Magnetic Resonance Spectroscopy in Mice Brain Data Using Low-Rank Denoising." Metabolites 12, no. 12 (2022): 1191. http://dx.doi.org/10.3390/metabo12121191.

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Magnetic resonance spectroscopy (MRS) is a noninvasive technique for measuring metabolite concentration. It can be used for preclinical small animal brain studies using rodents to provide information about neurodegenerative diseases and metabolic disorders. However, data acquisition from small volumes in a limited scan time is technically challenging due to its inherently low sensitivity. To mitigate this problem, this study investigated the feasibility of a low-rank denoising method in enhancing the quality of single voxel multinuclei (31P and 1H) MRS data at 9.4 T. Performance was evaluated

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Chen, Juan, Zhencai Zhu, Haiying Hu, Lin Qiu, Zhenzhen Zheng, and Lei Dong. "A Novel Adaptive Group Sparse Representation Model Based on Infrared Image Denoising for Remote Sensing Application." Applied Sciences 13, no. 9 (2023): 5749. http://dx.doi.org/10.3390/app13095749.

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Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoisin

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Yoon, Sang Min, Yeon Ju Lee, Gang-Joon Yoon, and Jungho Yoon. "Adaptive Total Variation Minimization-Based Image Enhancement from Flash and No-Flash Pairs." Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/319506.

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We present a novel approach for enhancing the quality of an image captured from a pair of flash and no-flash images. The main idea for image enhancement is to generate a new image by combining the ambient light of the no-flash image and the details of the flash image. In this approach, we propose a method based on Adaptive Total Variation Minimization (ATVM) so that it has an efficient image denoising effect by preserving strong gradients of the flash image. Some numerical results are presented to demonstrate the effectiveness of the proposed scheme.

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Burhan, Iman Mohammed, Rahman Nahi Abid, Mustafa Abdalkhudhur Jasim, and Refed Adnan Jaleel. "Improved Methods for Mammogram Breast Cancer Using by Denoising Filtering." Webology 19, no. 1 (2022): 1481–92. http://dx.doi.org/10.14704/web/v19i1/web19099.

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In diagnosing breast cancer, digital mammograms have shown their effectiveness as an appropriate and simple instrument in the early detection of tumor. Mammograms offer helpful cancer symptoms information, including microcalcifications and masses, which are not easy to distinguish because there are some flaws with the mammography images, including low contrast, high noise, fuzzy and blur. Additionally, there is a major problem with mammography because of a high density of the breast which conceals As a result of the mammographic image, it is more difficult to distinguish between the tissues wi

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Wang, Enning, and Jeff Nealon. "Applying machine learning to 3D seismic image denoising and enhancement." Interpretation 7, no. 3 (2019): SE131—SE139. http://dx.doi.org/10.1190/int-2018-0224.1.

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We have trained a supervised deep 3D convolutional neural network (CNN) on marine seismic images for poststack structural seismic image enhancement and noise attenuation. Rather than adding artificial noise to training inputs, the difference in noise levels between the training inputs and labels was created by shot density differences. This design enables the trained CNN to mimic the results and power of stacking to specifically target random and coherent migration artifacts while enhancing low-amplitude reflections. We used field seismic from multiple Gulf of Mexico surveys to train the CNN a

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Li, Huafeng, Xiaoge He, Dapeng Tao, Yuanyan Tang, and Ruxin Wang. "Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning." Pattern Recognition 79 (July 2018): 130–46. http://dx.doi.org/10.1016/j.patcog.2018.02.005.

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Palovcak, Eugene, Daniel Asarnow, Melody G. Campbell, Zanlin Yu, and Yifan Cheng. "Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks." IUCrJ 7, no. 6 (2020): 1142–50. http://dx.doi.org/10.1107/s2052252520013184.

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In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to den

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Wang, Luna, Liao Yu, Jun Zhu, Haoyu Tang, Fangfang Gou, and Jia Wu. "Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement." Healthcare 10, no. 8 (2022): 1468. http://dx.doi.org/10.3390/healthcare10081468.

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Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement.

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Tan, Hongjun, Dongxiu Ou, Lei Zhang, Guochen Shen, Xinghua Li, and Yuqing Ji. "Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes." Sensors 22, no. 15 (2022): 5835. http://dx.doi.org/10.3390/s22155835.

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Thermal imaging is an important technology in low-visibility environments, and due to the blurred edges and low contrast of infrared images, enhancement processing is of vital importance. However, to some extent, the existing enhancement algorithms based on pixel-level information ignore the salient feature of targets, the temperature which effectively separates the targets by their color. Therefore, based on the temperature and pixel features of infrared images, first, a threshold denoising model based on wavelet transformation with bilateral filtering (WTBF) was proposed. Second, our group p

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Jiang, Xiao, Haibin Yu, Yaxin Zhang, et al. "An Underwater Image Enhancement Method for a Preprocessing Framework Based on Generative Adversarial Network." Sensors 23, no. 13 (2023): 5774. http://dx.doi.org/10.3390/s23135774.

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This paper presents an efficient underwater image enhancement method, named ECO-GAN, to address the challenges of color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated de

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Shi, Xiangsheng, Xuefei Ning, Lidong Guo, et al. "Memory-Oriented Structural Pruning for Efficient Image Restoration." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2245–53. http://dx.doi.org/10.1609/aaai.v37i2.25319.

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Deep learning (DL) based methods have significantly pushed forward the state-of-the-art for image restoration (IR) task. Nevertheless, DL-based IR models are highly computation- and memory-intensive. The surging demands for processing higher-resolution images and multi-task paralleling in practical mobile usage further add to their computation and memory burdens. In this paper, we reveal the overlooked memory redundancy of the IR models and propose a Memory-Oriented Structural Pruning (MOSP) method. To properly compress the long-range skip connections (a major source of the memory burden), we

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Yao, Chao, Shuo Jin, Meiqin Liu, and Xiaojuan Ban. "Dense Residual Transformer for Image Denoising." Electronics 11, no. 3 (2022): 418. http://dx.doi.org/10.3390/electronics11030418.

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Image denoising is an important low-level computer vision task, which aims to reconstruct a noise-free and high-quality image from a noisy image. With the development of deep learning, convolutional neural network (CNN) has been gradually applied and achieved great success in image denoising, image compression, image enhancement, etc. Recently, Transformer has been a hot technique, which is widely used to tackle computer vision tasks. However, few Transformer-based methods have been proposed for low-level vision tasks. In this paper, we proposed an image denoising network structure based on Tr

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Huang, Liangliang, Huiyan Jiang, Shaojie Li, Zhiqi Bai, and Jitong Zhang. "Two stage residual CNN for texture denoising and structure enhancement on low dose CT image." Computer Methods and Programs in Biomedicine 184 (February 2020): 105115. http://dx.doi.org/10.1016/j.cmpb.2019.105115.

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Yan, Mengying, Danyang Qin, Gengxin Zhang, Huapeng Tang, and Lin Ma. "Nighttime Image Stitching Method Based on Image Decomposition Enhancement." Entropy 25, no. 9 (2023): 1282. http://dx.doi.org/10.3390/e25091282.

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Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light ar

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Zhang, Peng Lin, Zhi Qiang Zhao, and Peng Kong. "Study on Pretreatment Method of X-Ray Real-Time Imaging Digital Image." Advanced Materials Research 815 (October 2013): 854–59. http://dx.doi.org/10.4028/www.scientific.net/amr.815.854.

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X-ray nondestructive testing has a wide range of applications, which in materials testing, food testing, manufacturing, instrumentation, automotive parts and other fields having good performance. The paper mainly deals with low contrast X-ray digital images, image edge blur features and digital image preprocessing techniques of contrast. By a crack image taking geometric transformations, gray-scale transformations and image enhancement processing such as pretreatment technology airspace transforms, getting three options that have been able to effectively realize image denoising and enhancement

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Ge, Wei, Le Zhang, Weida Zhan, Jiale Wang, Depeng Zhu, and Yang Hong. "A Low-Illumination Enhancement Method Based on Structural Layer and Detail Layer." Entropy 25, no. 8 (2023): 1201. http://dx.doi.org/10.3390/e25081201.

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Low-illumination image enhancement technology is a topic of interest in the field of image processing. However, while improving image brightness, it is difficult to effectively maintain the texture and details of the image, and the quality of the image cannot be guaranteed. In order to solve this problem, this paper proposed a low-illumination enhancement method based on structural and detail layers. Firstly, we designed an SRetinex-Net model. The network is mainly divided into two parts: a decomposition module and an enhancement module. Second, the decomposition module mainly adopts the SU-Ne

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KHALDI, KAIS, MONIA TURKI-HADJ ALOUANE, and ABDEL-OUAHAB BOUDRAA. "VOICED SPEECH ENHANCEMENT BASED ON ADAPTIVE FILTERING OF SELECTED INTRINSIC MODE FUNCTIONS." Advances in Adaptive Data Analysis 02, no. 01 (2010): 65–80. http://dx.doi.org/10.1142/s1793536910000409.

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In this paper a new method for voiced speech enhancement combining the Empirical Mode Decomposition (EMD) and the Adaptive Center Weighted Average (ACWA) filter is introduced. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Since voiced speech structure is mostly distributed on both medium and low frequencies, the shorter scale IMFs of the noisy signal are beneath noise, however the longer scale ones are less noisy. Therefore, the main idea of the proposed approach is to only filter the shorter scale IMFs, and to keep the long

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Manwar, Rayyan, Matin Hosseinzadeh, Ali Hariri, Karl Kratkiewicz, Shahryar Noei, and Mohammad N. Avanaki. "Photoacoustic Signal Enhancement: Towards Utilization of Low Energy Laser Diodes in Real-Time Photoacoustic Imaging." Sensors 18, no. 10 (2018): 3498. http://dx.doi.org/10.3390/s18103498.

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In practice, photoacoustic (PA) waves generated with cost-effective and low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging is widely used to increase the signal-to-noise ratio (SNR) of PA signals; however, it is time consuming and in the case of very low SNR signals, hundreds to thousands of data acquisition epochs are needed. In this study, we explored the feasibility of using an adaptive and time-efficient filtering method to improve the SNR of PA signals. Our r

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Guo, Shiyao, Yuxia Sheng, Li Chai, and Jingxin Zhang. "Kernel graph filtering—A new method for dynamic sinogram denoising." PLOS ONE 16, no. 12 (2021): e0260374. http://dx.doi.org/10.1371/journal.pone.0260374.

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Low count PET (positron emission tomography) imaging is often desirable in clinical diagnosis and biomedical research, but its images are generally very noisy, due to the very weak signals in the sinograms used in image reconstruction. To address this issue, this paper presents a novel kernel graph filtering method for dynamic PET sinogram denoising. This method is derived from treating the dynamic sinograms as the signals on a graph, and learning the graph adaptively from the kernel principal components of the sinograms to construct a lowpass kernel graph spectrum filter. The kernel graph fil

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Huang, Hui, Xi’an Feng, and Jionghui Jiang. "Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features." Journal of Electrical and Computer Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6807473.

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According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancemen

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Huang, Shih-Chia, Quoc-Viet Hoang, Trung-Hieu Le, et al. "An Advanced Noise Reduction and Edge Enhancement Algorithm." Sensors 21, no. 16 (2021): 5391. http://dx.doi.org/10.3390/s21165391.

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Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments, especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised

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Dong, Xuan, Xiaoyan Hu, Weixin Li, Xiaojie Wang, and Yunhong Wang. "MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1264–72. http://dx.doi.org/10.1609/aaai.v35i2.16214.

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We study the High Dynamic Range (HDR) imaging problem using two Low Dynamic Range (LDR) images that are shot from dual-lens systems in a single shot time with different exposures. In most of the related HDR imaging methods, the problem is usually solved by Multiple Images Merging, i.e. the final HDR image is fused from pixels of all the input LDR images. However, ghost artifacts can be hardly avoided using this strategy. Instead of directly merging the multiple LDR inputs, we use an indirect way which enhances the main image, i.e. the short exposure image IS, using the long exposure image IL s

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del Ser, D., and O. Fors. "tfaw survey – I. Wavelet-based denoising of K2 light curves. Discovery and validation of two new Earth-sized planets in K2 campaign 1." Monthly Notices of the Royal Astronomical Society 498, no. 2 (2020): 2778–97. http://dx.doi.org/10.1093/mnras/staa2509.

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ABSTRACT The wavelet-based detrending and denoising method tfaw is applied for the first time to EVEREST 2.0-corrected light curves to further improve the photometric precision of almost all K2 observing campaigns (C1–C8, C12–C18). The performance of both methods is evaluated in terms of 6 h combined differential photometric precision (CDPP), simulated transit detection efficiency, and planet characterization in different SNR regimes. On average, tfaw median 6 h CDPP is ${\sim} 30{\rm {per \, cent}}$ better than the one achieved by EVEREST 2.0 for all observing campaigns. Using the transit lea

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Tsagkatakis, Grigorios, Anastasia Aidini, Konstantina Fotiadou, Michalis Giannopoulos, Anastasia Pentari, and Panagiotis Tsakalides. "Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement." Sensors 19, no. 18 (2019): 3929. http://dx.doi.org/10.3390/s19183929.

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Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount

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Huo, Fu Rong, He Li, Yu Hang Yang, Chang Xi Xue, and Wen Sheng Wang. "Imaging Analysis and Application of Digital Speckle Photography with EALCD." Applied Mechanics and Materials 333-335 (July 2013): 1007–12. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1007.

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According to the principle of speckle photography, CCD(Charge-Coupled Device) as a recorder, and EALCD(Electrically Addressed Liquid Crystal Display) as a read-out element, which makes the speckle photography to digital. Recording light path of the subjective and the objective speckle and observation light path of full-field analysis and point-by-point analysis for fringe reconstruction have been respectively researched. At the same time, measuring by speckle photography, the fringes in the interometry pattern must be carefully analyzed. Since the speckle noise can greatly infect the signals.

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