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Journal articles on the topic 'Image synthesis and analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Englmeier, K. H., M. Haubner, U. Fink, and B. Fink. "Image analysis and synthesis of multimodal images in medicine." Computer Methods and Programs in Biomedicine 43, no. 3-4 (June 1994): 193–206. http://dx.doi.org/10.1016/0169-2607(94)90070-1.

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2

ZHAO, YUTAO, MING ZHANG, and YUNCAI LIU. "FLOW VIDEO SYNTHESIS FROM AN IMAGE." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 03 (May 2010): 421–31. http://dx.doi.org/10.1142/s0218001410007981.

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A new method of image based flow analysis and synthesis from a still image is presented. We analyze the flow parts from a still image in order to get continuous video by image matting, image inpainting, projecting flow field onto the image and modulation. We construct and project 3D flow models onto still images and propose 2D modulation methods that are more suitable and practical to our synthesis. Our technique can edit still images that have flow parts. The experiments show the method is effective.

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3

Merianos, Ioannis, and Nikolaos Mitianoudis. "Multiple-Exposure Image Fusion for HDR Image Synthesis Using Learned Analysis Transformations." Journal of Imaging 5, no. 3 (February 26, 2019): 32. http://dx.doi.org/10.3390/jimaging5030032.

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Modern imaging applications have increased the demand for High-Definition Range (HDR) imaging. Nonetheless, HDR imaging is not easily available with low-cost imaging sensors, since their dynamic range is rather limited. A viable solution to HDR imaging via low-cost imaging sensors is the synthesis of multiple-exposure images. A low-cost sensor can capture the observed scene at multiple-exposure settings and an image-fusion algorithm can combine all these images to form an increased dynamic range image. In this work, two image-fusion methods are combined to tackle multiple-exposure fusion. The luminance channel is fused using the Mitianoudis and Stathaki (2008) method, while the color channels are combined using the method proposed by Mertens et al. (2007). The proposed fusion algorithm performs well without halo artifacts that exist in other state-of-the-art methods. This paper is an extension version of a conference, with more analysis on the derived method and more experimental results that confirm the validity of the method.

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Phankokkruad, Manop, and Wacharawichanant Sirirat. "Morphological Analysis of Particles in Transmission Electron Microscopy Image Using Image Processing." Advanced Materials Research 774-776 (September 2013): 1262–66. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1262.

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The analysis of material particles in the transmission electron microscopy (TEM) image is the most important in the development and synthesis of the new materials for application in many fields. Manual identification is the hard work, spent a lot of time and inaccurate method. To solve this problem, the image processing is proposed for automatically identifies the material particles boundary and size in TEM images in or order to extract the useful data. The image processing method has been experimented on variety of TEM images and very promising results have been achieved given more accuracy. Experimental results show that the proposed method gave the high adaptability, more accurate and rapidly than the manual method.

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Lee, Dong-Gun, Yonghun Jang, and Yeong-Seok Seo. "Intelligent Image Synthesis for Accurate Retinal Diagnosis." Electronics 9, no. 5 (May 7, 2020): 767. http://dx.doi.org/10.3390/electronics9050767.

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Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using the multilayer perceptron concept with artificial neural networks. The approach proposed in this study can convert vessel images to vessel-centered images with clearer identification, even for low-resolution retinal images. To verify the proposed approach, we determined whether high-resolution vessel images could be extracted from low-resolution images through a statistical analysis using high- and low-resolution images extracted from the same patient.

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TSUMURA, Norimichi. "Image-Based Skin Analysis and Appearance Synthesis." Oleoscience 7, no. 7 (2007): 267–72. http://dx.doi.org/10.5650/oleoscience.7.267.

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7

Smith, M. J. T., and S. L. Eddins. "Analysis/synthesis techniques for subband image coding." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 8 (1990): 1446–56. http://dx.doi.org/10.1109/29.57579.

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8

Han, Yu, Huiqian Du, Fan Lam, Wenbo Mei, and Liping Fang. "Image Reconstruction Using Analysis Model Prior." Computational and Mathematical Methods in Medicine 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7571934.

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The analysis model has been previously exploited as an alternative to the classical sparse synthesis model for designing image reconstruction methods. Applying a suitable analysis operator on the image of interest yields a cosparse outcome which enables us to reconstruct the image from undersampled data. In this work, we introduce additional prior in the analysis context and theoretically study the uniqueness issues in terms of analysis operators in general position and the specific 2D finite difference operator. We establish bounds on the minimum measurement numbers which are lower than those in cases without using analysis model prior. Based on the idea of iterative cosupport detection (ICD), we develop a novel image reconstruction model and an effective algorithm, achieving significantly better reconstruction performance. Simulation results on synthetic and practical magnetic resonance (MR) images are also shown to illustrate our theoretical claims.

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9

Yuan, Sheng, Yuting Chen, Huihui Huo, and Li Zhu. "Analysis and Synthesis of Traffic Scenes from Road Image Sequences." Sensors 20, no. 23 (December 4, 2020): 6939. http://dx.doi.org/10.3390/s20236939.

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Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic elements detection, road scene inpainting, and road scene reconstruction. First, a new bidirectional single shot multi-box detector (BiSSD) method is designed with a global context attention mechanism for traffic elements detection. After the detection of traffic elements, an unsupervised CycleGAN is applied to inpaint the occlusion regions with optical flow. The high-quality inpainting images are then obtained by the proposed image inpainting algorithm. Finally, a traffic scene simulation method is developed by integrating the foreground and background elements of traffic scenes. The extensive experiments and comparisons demonstrate the effectiveness of the proposed framework.

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10

Chang Seek Choi, K. Aizawa, H. Harashima, and T. Takebe. "Analysis and synthesis of facial image sequences in model-based image coding." IEEE Transactions on Circuits and Systems for Video Technology 4, no. 3 (June 1994): 257–75. http://dx.doi.org/10.1109/76.305871.

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11

Yang, Meng, Heyou Chang, and Weixin Luo. "Discriminative analysis-synthesis dictionary learning for image classification." Neurocomputing 219 (January 2017): 404–11. http://dx.doi.org/10.1016/j.neucom.2016.09.037.

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12

Pun, Thierry, and Edwin Blake. "Relationships between Image Synthesis and Analysis Towards Unification?." Computer Graphics Forum 9, no. 2 (June 1990): 149–63. http://dx.doi.org/10.1111/j.1467-8659.1990.tb00387.x.

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13

Becker, D. E., H. Ancin, B. Roysam, and J. N. Turner. "Fast automated mosaic synthesis method for 2-D/3-D image analysis of specimens much wider than the field of view." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 224–25. http://dx.doi.org/10.1017/s0424820100168852.

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We present an efficient, robust, and widely-applicable technique for computational synthesis of wide-area images from a series of overlapping partial views. The synthesized image is the set union of the areas covered by the partial views, and is called the “mosaic”. One application is the laser-scanning confocal microscopy of specimens that are much wider than the field of view of the microscope. Another is imaging of the retinal periphery using a standard fundus imager. This technique can also be used to combine the results of various forms of image analysis, such as cell counting and neuron tracing, to generate large representations that are equivalent to processing the total mosaic, rather than the individual partial views.The synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the application. For instance, in the retinal imaging application, the vascular branching and crossover points are a natural choice. Likewise, the locations of cells in Figs. 1 and 2 provide a natural set of landmarks for joining these images.

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14

Keller, Alexander. "Hierarchical Monte Carlo image synthesis." Mathematics and Computers in Simulation 55, no. 1-3 (February 2001): 79–92. http://dx.doi.org/10.1016/s0378-4754(00)00248-2.

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15

Chinarro, E., B. Moreno, D. Martín, L. González, E. Villanueva, D. Guinea, and J. R. Jurado. "Posibilidades del análisis de imagen para el estudio de la síntesis de materiales por combustión." Boletín de la Sociedad Española de Cerámica y Vidrio 44, no. 2 (April 30, 2005): 105–12. http://dx.doi.org/10.3989/cyv.2005.v44.i2.391.

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Klaser, Kerstin, Pedro Borges, Richard Shaw, Marta Ranzini, Marc Modat, David Atkinson, Kris Thielemans, et al. "A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis." Applied Sciences 11, no. 4 (February 12, 2021): 1667. http://dx.doi.org/10.3390/app11041667.

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Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.

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17

Wallis, Christopher G. R., Yves Wiaux, and Jason D. McEwen. "Sparse Image Reconstruction on the Sphere: Analysis and Synthesis." IEEE Transactions on Image Processing 26, no. 11 (November 2017): 5176–87. http://dx.doi.org/10.1109/tip.2017.2716824.

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18

Vasilescu, M., and Demetri Terzopoulos. "Multilinear (Tensor) Image Synthesis, Analysis and Recognition [DSP Exploratory]." IEEE Signal Processing Magazine 24, no. 99 (2007): 118–23. http://dx.doi.org/10.1109/msp.2007.4317476.

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19

Vasilescu, M., and Demetri Terzopoulos. "Multilinear (Tensor) Image Synthesis, Analysis, and Recognition [Exploratory DSP]." IEEE Signal Processing Magazine 24, no. 6 (November 2007): 118–23. http://dx.doi.org/10.1109/msp.2007.906024.

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20

Wang, D., D. J. Kerbyson, G. J. King, and G. R. Nudd. "Realistic image synthesis of plant structures for genetic analysis." Image and Vision Computing 19, no. 8 (May 2001): 517–22. http://dx.doi.org/10.1016/s0262-8856(00)00094-9.

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21

Shah, Aayush A., Benjamin Schultz, Kevin L. Kohlstedt, Sharon C. Glotzer, and Michael J. Solomon. "Synthesis, Assembly, and Image Analysis of Spheroidal Patchy Particles." Langmuir 29, no. 15 (April 2, 2013): 4688–96. http://dx.doi.org/10.1021/la400317t.

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22

Sagiv, Chen, Nir A. Sochen, and Yehoshua Y. Zeevi. "Two-dimensional affine frames for image analysis and synthesis." Applied and Computational Harmonic Analysis 24, no. 2 (March 2008): 174–94. http://dx.doi.org/10.1016/j.acha.2007.06.008.

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23

Cadzow, J. A., D. M. Wilkes, R. A. Peters, and X. Li. "Image texture synthesis-by-analysis using moving-average models." IEEE Transactions on Aerospace and Electronic Systems 29, no. 4 (1993): 1110–22. http://dx.doi.org/10.1109/7.259515.

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24

Liu, Yang, Lu Meng, and Jianping Zhong. "MAGAN: Mask Attention Generative Adversarial Network for Liver Tumor CT Image Synthesis." Journal of Healthcare Engineering 2021 (January 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/6675259.

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For deep learning, the size of the dataset greatly affects the final training effect. However, in the field of computer-aided diagnosis, medical image datasets are often limited and even scarce. We aim to synthesize medical images and enlarge the size of the medical image dataset. In the present study, we synthesized the liver CT images with a tumor based on the mask attention generative adversarial network (MAGAN). We masked the pixels of the liver tumor in the image as the attention map. And both the original image and attention map were loaded into the generator network to obtain the synthesized images. Then, the original images, the attention map, and the synthesized images were all loaded into the discriminator network to determine if the synthesized images were real or fake. Finally, we can use the generator network to synthesize liver CT images with a tumor. The experiments showed that our method outperformed the other state-of-the-art methods and can achieve a mean peak signal-to-noise ratio (PSNR) of 64.72 dB. All these results indicated that our method can synthesize liver CT images with a tumor and build a large medical image dataset, which may facilitate the progress of medical image analysis and computer-aided diagnosis. An earlier version of our study has been presented as a preprint in the following link: https://www.researchsquare.com/article/rs-41685/v1.

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25

KURODA, Tsutomu, and Tomio WATANABE. "Facial Color Image Analysis and Synthesis for the Virtual Face Image in Emotional Change." Transactions of the Japan Society of Mechanical Engineers Series C 65, no. 638 (1999): 4114–20. http://dx.doi.org/10.1299/kikaic.65.4114.

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26

Sull, S., and N. Ahuja. "Integrated 3-D analysis and analysis-guided synthesis of flight image sequences." IEEE Transactions on Pattern Analysis and Machine Intelligence 16, no. 4 (April 1994): 357–72. http://dx.doi.org/10.1109/34.277590.

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27

Becker, D. E. "Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes." Proceedings, annual meeting, Electron Microscopy Society of America 53 (August 13, 1995): 650–51. http://dx.doi.org/10.1017/s0424820100139627.

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An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

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28

HEIJMANS, HENK J. A. M., GEMMA PIELLA, and BÉATRICE PESQUET-POPESCU. "ADAPTIVE WAVELETS FOR IMAGE COMPRESSION USING UPDATE LIFTING: QUANTIZATION AND ERROR ANALYSIS." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 01 (March 2006): 41–63. http://dx.doi.org/10.1142/s0219691306001087.

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Classical linear wavelet representations of images have the drawback that they are not optimally suited to represent edge information. To overcome this problem, nonlinear multiresolution decompositions have been designed to take into account the characteristics of the input signal/image. In our previous work20,22,23 we have introduced an adaptive lifting framework, that does not require bookkeeping but has the property that it processes edges and homogeneous image regions in a different fashion. The current paper discusses the effects of quantization in such an adaptive wavelet decomposition. We provide conditions for recovering the original decisions at the synthesis and for relating the reconstruction error to the quantization error. Such an analysis is essential for the application of these adaptive decompositions in image compression.

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29

Meer, Peter, Ernest S. Baugher, and Azriel Rosenfeld. "Frequency Domain Analysis and Synthesis of Image Pyramid Generating Kernels." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 4 (July 1987): 512–22. http://dx.doi.org/10.1109/tpami.1987.4767939.

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30

KURODA, Tsutomu, and Tomio WATANABE. "Analysis and Synthesis of Facial Color Using Color Image Processing." JSME International Journal Series C 41, no. 2 (1998): 307–12. http://dx.doi.org/10.1299/jsmec.41.307.

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KURODA, Tsutomu, and Tomio WATANABE. "Analysis and Synthesis of Facial Color Using Color Image Processing." Transactions of the Japan Society of Mechanical Engineers Series C 63, no. 608 (1997): 1255–60. http://dx.doi.org/10.1299/kikaic.63.1255.

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32

Shen, Hui-Liang, and John H. Xin. "Analysis and synthesis of multicolored objects in a single image." Optics Letters 30, no. 18 (September 15, 2005): 2378. http://dx.doi.org/10.1364/ol.30.002378.

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33

Eisert, Peter, and Jürgen Rurainsky. "Geometry-assisted image-based rendering for facial analysis and synthesis." Signal Processing: Image Communication 21, no. 6 (July 2006): 493–505. http://dx.doi.org/10.1016/j.image.2006.03.003.

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34

Arinin, V. A. "Histogram analysis and synthesis in processing series of images: Extending the concept of image similarity." Pattern Recognition and Image Analysis 18, no. 4 (December 2008): 588–91. http://dx.doi.org/10.1134/s1054661808040081.

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35

Carrington, W. A., F. S. Fay, K. E. Fogarty, and L. Lifshitz. "Analysis of 3-d molecular distribution with the digital imaging microscope." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 40–41. http://dx.doi.org/10.1017/s0424820100102286.

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Advances in digital imaging microscopy and in the synthesis of fluorescent dyes allow the determination of 3D distribution of specific proteins, ions, GNA or DNA in single living cells. Effective use of this technology requires a combination of optical and computer hardware and software for image restoration, feature extraction and computer graphics.The digital imaging microscope consists of a conventional epifluorescence microscope with computer controlled focus, excitation and emission wavelength and duration of excitation. Images are recorded with a cooled (-80°C) CCD. 3D images are obtained as a series of optical sections at .25 - .5 μm intervals.A conventional microscope has substantial blurring along its optical axis. Out of focus contributions to a single optical section cause low contrast and flare; details are poorly resolved along the optical axis. We have developed new computer algorithms for reversing these distortions. These image restoration techniques and scanning confocal microscopes yield significantly better images; the results from the two are comparable.

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36

Mourdikoudis, Stefanos, Thomas Altantzis, Luis M. Liz-Marzán, Sara Bals, Isabel Pastoriza-Santos, and Jorge Pérez-Juste. "Hydrophilic Pt nanoflowers: synthesis, crystallographic analysis and catalytic performance." CrystEngComm 18, no. 19 (2016): 3422–27. http://dx.doi.org/10.1039/c6ce00039h.

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37

Son, Dong-Min, Hyuk-Ju Kwon, and Sung-Hak Lee. "Visible and Near-Infrared Image Synthesis Using PCA Fusion of Multiscale Layers." Applied Sciences 10, no. 23 (December 4, 2020): 8702. http://dx.doi.org/10.3390/app10238702.

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This study proposes a method of blending visible and near-infrared (NIR) images to enhance their edge details and local contrast based on the Laplacian pyramid and principal component analysis (PCA). In the proposed method, both the Laplacian pyramid and PCA are implemented to generate a radiance map. Using the PCA algorithm, the soft-mixing method and the mask-skipping filter were applied when the images were fused. The color compensation method uses the ratio between the radiance map fused by the Laplacian pyramid and the PCA algorithm and the luminance channel of the visible image to preserve the chrominance of the visible image. The results show that the proposed method improves edge details and local contrast effectively.

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Singh, Abhishek, Debojyoti Dutta, and Amit Saha. "MIGAN: Malware Image Synthesis Using GANs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10033–34. http://dx.doi.org/10.1609/aaai.v33i01.330110033.

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Majority of the advancement in Deep learning (DL) has occurred in domains such as computer vision, and natural language processing, where abundant training data is available. A major obstacle in leveraging DL techniques for malware analysis is the lack of sufficiently big, labeled datasets. In this paper, we take the first steps towards building a model which can synthesize labeled dataset of malware images using GAN. Such a model can be utilized to perform data augmentation for training a classifier. Furthermore, the model can be shared publicly for community to reap benefits of dataset without sharing the original dataset. First, we show the underlying idiosyncrasies of malware images and why existing data augmentation techniques as well as traditional GAN training fail to produce quality artificial samples. Next, we propose a new method for training GAN where we explicitly embed prior domain knowledge about the dataset into the training procedure. We show improvements in training stability and sample quality assessed on different metrics. Our experiments show substantial improvement on baselines and promise for using such a generative model for malware visualization systems.

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Kim, Myoung-Jun. "An Image-based Color Appearance Analysis of Makeup and Image Synthesis based on Kubelka-Munk Model." Journal of Korea Multimedia Society 18, no. 3 (March 30, 2015): 349–58. http://dx.doi.org/10.9717/kmms.2015.18.3.349.

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CHEN, SHAOKANG, BRIAN C. LOVELL, and TING SHAN. "ROBUST ADAPTED PRINCIPAL COMPONENT ANALYSIS FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 03 (May 2009): 491–520. http://dx.doi.org/10.1142/s0218001409007284.

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Recognizing faces with uncontrolled pose, illumination, and expression is a challenging task due to the fact that features insensitive to one variation may be highly sensitive to the other variations. Existing techniques dealing with just one of these variations are very often unable to cope with the other variations. The problem is even more difficult in applications where only one gallery image per person is available. In this paper, we describe a recognition method, Adapted Principal Component Analysis (APCA), that can simultaneously deal with large variations in both illumination and facial expression using only a single gallery image per person. We have now extended this method to handle head pose variations in two steps. The first step is to apply an Active Appearance Model (AAM) to the non-frontal face image to construct a synthesized frontal face image. The second is to use APCA for classification robust to lighting and pose. The proposed technique is evaluated on three public face databases — Asian Face, Yale Face, and FERET Database — with images under different lighting conditions, facial expressions, and head poses. Experimental results show that our method performs much better than other recognition methods including PCA, FLD, PRM and LTP. More specifically, we show that by using AAM for frontal face synthesis from high pose angle faces, the recognition rate of our APCA method increases by up to a factor of 4.

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41

Li, Xuelu, Vishal Monga, and U. K. Arvind Rao. "Analysis–Synthesis Learning With Shared Features: Algorithms for Histology Image Classification." IEEE Transactions on Biomedical Engineering 67, no. 4 (April 2020): 1061–73. http://dx.doi.org/10.1109/tbme.2019.2928997.

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42

Ojima, Nobutoshi, and Koji Minami. "Image Analysis and Synthesis of Faces Applied with Make-up Foundation." Journal of The Institute of Image Information and Television Engineers 62, no. 12 (2008): 1928–32. http://dx.doi.org/10.3169/itej.62.1928.

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43

Chang, Heyou, Hui Tang, Fanlong Zhang, Yang Chen, and Hao Zheng. "Graph-Regularized Discriminative Analysis-Synthesis Dictionary Pair Learning for Image Classification." IEEE Access 7 (2019): 55398–406. http://dx.doi.org/10.1109/access.2019.2912932.

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44

Yan, Ronghua, Jinye Peng, Dongmei Ma, and Desheng Wen. "Spectral Tensor Synthesis Analysis for Hyperspectral Image Spectral–Spatial Feature Extraction." Journal of the Indian Society of Remote Sensing 47, no. 1 (October 28, 2018): 91–100. http://dx.doi.org/10.1007/s12524-018-0873-0.

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Li, Xuesong, Guo Cao, Youqiang Zhang, Ayesha Shafique, and Peng Fu. "Combining synthesis sparse with analysis sparse for single image super-resolution." Signal Processing: Image Communication 83 (April 2020): 115805. http://dx.doi.org/10.1016/j.image.2020.115805.

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46

Dosil, Raquel, Xosé M. Pardo, and Xosé R. Fdez-Vidal. "Data-driven synthesis of composite-feature detectors for 3D image analysis." Image and Vision Computing 24, no. 3 (March 2006): 225–38. http://dx.doi.org/10.1016/j.imavis.2005.11.005.

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47

Liu, Wen, Feng Qiu, and Xi Zeng. "Art Effect of Photographic Darkroom Stunt Simulation Based on Mobile Computing to Synthesize Images across Image Moving Selected Regions." Wireless Communications and Mobile Computing 2021 (July 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/2194985.

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Prior to the advent of digital image processing technology, image composition primarily used human vision to identify colors and artificially convert them. However, manually synthesizing and transforming graphics images will not only consume a lot of manpower, time, and energy but also due to manual limitations in the process of synthesizing and coloring the pictures, the resulting pictures cannot meet people’s needs. In order to improve the speed and quality of image synthesis, and to synthesize the pictures people need more quickly and accurately, this article synthesizes the image based on the movement calculation across the selected area of the image and analyzes the photographic darkroom special effects of the synthesized image to simulate the artistic effect. Using case analysis method, literature analysis method, and other methods, the database was collected and a model of photographic darkroom stunt simulation artistic effect recognition was built. The results of the study found that the composite image based on the movement calculation across the selected area of the image is better than the composite image of other algorithms, and the quality of hue and saturation is more than 30% higher than other synthesis methods. It should be verified by experiments. The results are significantly different. This shows that the composite image based on moving calculation across the selected area of the image can achieve good results in the photographic darkroom stunt simulation artistic effect.

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Helmer, Dorothea, Kevin Brahm, Christian Helmer, Julia Susanne Wack, Gerald Brenner-Weiss, and Katja Schmitz. "Two-channel image analysis method for the screening of OBOC libraries." Analytical Methods 8, no. 20 (2016): 4142–52. http://dx.doi.org/10.1039/c5ay02981c.

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Chow, Yunshyong. "A limit theorem for pattern synthesis in image processing." Journal of Multivariate Analysis 34, no. 1 (July 1990): 75–94. http://dx.doi.org/10.1016/0047-259x(90)90062-m.

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50

BEHNKE, SVEN. "LEARNING ITERATIVE IMAGE RECONSTRUCTION IN THE NEURAL ABSTRACTION PYRAMID." International Journal of Computational Intelligence and Applications 01, no. 04 (December 2001): 427–38. http://dx.doi.org/10.1142/s1469026801000342.

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Abstract:

Successful image reconstruction requires the recognition of a scene and the generation of a clean image of that scene. We propose to use recurrent neural networks for both analysis and synthesis. The networks have a hierarchical architecture that represents images in multiple scales with different degrees of abstraction. The mapping between these representations is mediated by a local connection structure. We supply the networks with degraded images and train them to reconstruct the originals iteratively. This iterative reconstruction makes it possible to use partial results as context information to resolve ambiguities. We demonstrate the power of the approach using three examples: superresolution, fill-in of occluded parts, and noise removal/contrast enhancement. We also reconstruct images from sequences of degraded images.

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