Artigos de revistas: "Image problem"

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Miodownik, Mark. "Image problem". Materials Today 8, n.º 11 (novembro de 2005): 6. http://dx.doi.org/10.1016/s1369-7021(05)71133-0.

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Shareefunnisa, Syed, e M. Bhargavi. "Solving Image Thresholding Problem Using Hybrid Algorithm". International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (31 de dezembro de 2017): 736–40. http://dx.doi.org/10.31142/ijtsrd7021.

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Shin, Chang Jong, Tae Bok Lee e Yong Seok Heo. "Dual Image Deblurring Using Deep Image Prior". Electronics 10, n.º 17 (24 de agosto de 2021): 2045. http://dx.doi.org/10.3390/electronics10172045.

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Blind image deblurring, one of the main problems in image restoration, is a challenging, ill-posed problem. Hence, it is important to design a prior to solve it. Recently, deep image prior (DIP) has shown that convolutional neural networks (CNNs) can be a powerful prior for a single natural image. Previous DIP-based deblurring methods exploited CNNs as a prior when solving the blind deburring problem and performed remarkably well. However, these methods do not completely utilize the given multiple blurry images, and have limitations of performance for severely blurred images. This is because their architectures are strictly designed to utilize a single image. In this paper, we propose a method called DualDeblur, which uses dual blurry images to generate a single sharp image. DualDeblur jointly utilizes the complementary information of multiple blurry images to capture image statistics for a single sharp image. Additionally, we propose an adaptive L2_SSIM loss that enhances both pixel accuracy and structural properties. Extensive experiments show the superior performance of our method to previous methods in both qualitative and quantitative evaluations.

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Abd-Alzahra, Zainab, e Basad Al-Sarray. "Medical Image Denoising Via Matrix Norm Minimization Problems". Al-Nahrain Journal of Science 24, n.º 2 (1 de junho de 2021): 72–77. http://dx.doi.org/10.22401/anjs.24.2.10.

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This paper presents the matrix completion problem for image denoising. Three problems based on matrix norm are performing: Spectral norm minimization problem (SNP), Nuclear norm minimization problem (NNP), and Weighted nuclear norm minimization problem (WNNP). In general, images representing by a matrix this matrix contains the information of the image, some information is irrelevant or unfavorable, so to overcome this unwanted information in the image matrix, information completion is used to comperes the matrix and remove this unwanted information. The unwanted information is handled by defining {0,1}-operator under some threshold. Applying this operator on a given matrix keeps the important information in the image and removing the unwanted information by solving the matrix completion problem that is defined by P. The quadratic programming use to solve the given three norm-based minimization problems. To improve the optimal solution a weighted exponential is used to compute the weighted vector of spectral that use to improve the threshold of optimal low rank that getting from solving the nuclear norm and spectral norm problems. The result of applying the proposed method on different types of images is given by adopting some metrics. The results showed the ability of the given methods.

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Radzimski, Z., T. Humphreys e J. Russ. "Revealing depth information by processing EBIC images". Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 850–51. http://dx.doi.org/10.1017/s0424820100106314.

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The conventional use and interpretation of electron beam induced current (EBIC) images in the SEM suffer from several inherent problems. One such problem is the superposition of topographic details, e.g., scratches, dust, contact damage, on the subsurface EBIC signal. Moreover, the use of an accelerating voltage to select depth may not distinguish information from regions close in depth. This exists in depth dependent defect imaging in multi-layer heteroepitaxial structures where two or more defect layers may overlap in the EBIC image.To alleviate these problems, techniques of image processing may be employed. By digitizing and storing several images, for example in the first problem EBIC and secondary electron image and in the second problem EBIC images obtained at different accelerating voltages, it is possible to use image differencing to extract a more selective image that reveals the desired information. In principle, image differencing requires simple subtraction (or division) of the brightness of each pixel in one image from that in the second.

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Wang, Yang, Snezana Mitrovic Minic, Robert Leitch e Abraham P. Punnen. "A GRASP for Next Generation Sapphire Image Acquisition Scheduling". International Journal of Aerospace Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3518537.

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This paper investigates an image acquisition scheduling problem for a Canadian surveillance-of-space satellite named Sapphire that takes images of deep space Earth-orbiting objects. For a set of resident space objects (RSOs) that needs to be imaged within the time horizon of one day, the Sapphire image acquisition scheduling (SIAS) problem is to find a schedule that maximizes the “Figure of Merit” of all the scheduled RSO images. To address the problem, we propose an effective GRASP heuristic that alternates between a randomized greedy constructive procedure and a local search procedure. Experimental comparisons with the currently used greedy algorithm are presented to demonstrate the merit of the proposed algorithm in handling the SIAS problem.

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Sheridan, Greg. "Asean: An Image Problem". Southeast Asian Affairs 1998 1998, n.º 1 (abril de 1998): 37–44. http://dx.doi.org/10.1355/seaa98c.

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Böröczky, Károly J., Erwin Lutwak, Deane Yang, Gaoyong Zhang e Yiming Zhao. "The Gauss Image Problem". Communications on Pure and Applied Mathematics 73, n.º 7 (6 de maio de 2020): 1406–52. http://dx.doi.org/10.1002/cpa.21898.

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Larochelle, Stéphane. "Solving XFEL's image problem". Nature Methods 12, n.º 5 (29 de abril de 2015): 392. http://dx.doi.org/10.1038/nmeth.3391.

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KOLATA, G. "A Math Image Problem". Science 232, n.º 4754 (30 de maio de 1986): 1087–88. http://dx.doi.org/10.1126/science.232.4754.1087-a.

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Cavuoto, J. "Neural Engineering's Image Problem". IEEE Spectrum 41, n.º 4 (abril de 2004): 32–37. http://dx.doi.org/10.1109/mspec.2004.1279191.

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Khan, Aamir, Weidong Jin, Amir Haider, MuhibUr Rahman e Desheng Wang. "Adversarial Gaussian Denoiser for Multiple-Level Image Denoising". Sensors 21, n.º 9 (24 de abril de 2021): 2998. http://dx.doi.org/10.3390/s21092998.

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Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.

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Then, P., e Y. C. Wang. "PERCEIVING DIGITAL WATERMARK DETECTION AS IMAGE CLASSIFICATION PROBLEM". Journal of IT in Asia 2, n.º 1 (26 de abril de 2016): 1–22. http://dx.doi.org/10.33736/jita.52.2007.

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Digital watermark detection is treated as classification problem of image processing. For image classification that searches for a butterfly, an image can be classified as positive class that is a butterfly and negative class that is not a butterfly. Similarly, the watermarked and unwatermarked images are perceived as positive and negative class respectively. Hence, Support Vector Machine (SVM) is used as the classifier of watermarked and unwatermarked digital image due to its ability of separating both linearly and non-linearly separable data. Hyperplanes of various detectors are briefly elaborated to show how SVM's hyperplane is suitable for Stirmark attacked watermarked image. Cox’s spread spectrum watermarking scheme is used to embed the watermark into digital images. Then, Support Vector Machine is trained with both the watermarked and unwatermarked images. Training SVM eliminates the use of watermark during the detection process. Receiver Operating Characteristics (ROC) graphs are plotted to assess the false positive and false negative probability of both the correlation detector of the watermarking schemes and SVM classifier. Both watermarked and unwatermarked images are later attacked under Stirmark, and then tested on the correlation detector and SVM classifier. Remedies are suggested to preprocess the training data. The optimal setting of SVM parameters is also investigated and determined besides preprocessing. The preprocessing and optimal parameters setting enable the trained SVM to achieve substantially better results than those resulting from the correlation detector.

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BOKKA, V., H. GURLA, S. OLARIU, J. L. SCHWING e I. STOJMENOVIĆ. "TIME-OPTIMAL DIGITAL GEOMETRY ALGORITHMS ON MESHES WITH MULTIPLE BROADCASTING". International Journal of Pattern Recognition and Artificial Intelligence 09, n.º 04 (agosto de 1995): 601–13. http://dx.doi.org/10.1142/s0218001495000225.

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The main contribution of this work is to show that a number of digital geometry problems can be solved elegantly on meshes with multiple broadcasting by using a time-optimal solution to the leftmost one problem as a basic subroutine. Consider a binary image pretiled onto a mesh with multiple broadcasting of size [Formula: see text] one pixel per processor. Our first contribution is to prove an Ω(n1/6) time lower bound for the problem of deciding whether the image contains at least one black pixel. We then obtain time lower bounds for many other digital geometry problems by reducing this fundamental problem to all the other problems of interest. Specifically, the problems that we address are: detecting whether an image contains at least one black pixel, computing the convex hull of the image, computing the diameter of an image, deciding whether a set of digital points is a digital line, computing the minimum distance between two images, deciding whether two images are linearly separable, computing the perimeter, area and width of a given image. Our second contribution is to show that the time lower bounds obtained are tight by exhibiting simple O(n1/6) time algorithms for these problems. As previously mentioned, an interesting feature of these algorithms is that they use, directly or indirectly, an algorithm for the leftmost one problem recently developed by one of the authors.

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Lineberger, Dan, Frank Dainello, John Jackman e Marvin Miller. "Web-based Tool for Diagnosing Cucurbit Disorders". HortScience 33, n.º 4 (julho de 1998): 597c—597. http://dx.doi.org/10.21273/hortsci.33.4.597c.

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Plant problems often are diagnosed by comparing the problem in hand to a set of color photographs of known symptoms. Color photographs are expensive and time consuming to publish and distribution of books and pamphlets is costly. Delivery of high resolution color photographs of common plant disorders via the World Wide Web is a cost-effective alternative. A web-based diagnostic resource has been created to assist problem identification of cucurbit disorders including nutritional, disease, and insect problems. The diagnostic tool consists of arrays of high resolution, color images grouped by similarity of appearance. The image arrays are clickable image maps, and the user is provided with increasingly detailed information and larger images as images are selected. At the final selection, the user is presented with a full screen image and text information describing the identity and control recommendations for the problem illustrated. This tool is intended to allow experienced diagnosticians to confirm a problem diagnosis, and to aid less experienced individuals in making proper diagnoses.

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Liang, Hong, Ankang Yu, Mingwen Shao e Yuru Tian. "Multi-Feature Guided Low-Light Image Enhancement". Applied Sciences 11, n.º 11 (29 de maio de 2021): 5055. http://dx.doi.org/10.3390/app11115055.

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Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.

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Huang, Zhiwu, Jiqing Wu e Luc Van Gool. "Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 3886–93. http://dx.doi.org/10.1609/aaai.v33i01.33013886.

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Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to achieve a tractable objective under the WGAN framework. In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we experimentally demonstrate the proposed manifold-aware WGAN model can generate more plausible manifold-valued images than its competitors.

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LEE, CHUNG-MONG, TING-CHUEN PONG e JAMES R. SLAGLE. "A KNOWLEDGE-BASED SYSTEM FOR THE IMAGE CORRESPONDENCE PROBLEM". International Journal of Pattern Recognition and Artificial Intelligence 04, n.º 01 (março de 1990): 45–55. http://dx.doi.org/10.1142/s0218001490000046.

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The image correspondence problem has generally been considered the most difficult step in both stereo and temporal vision. Most existing approaches match area features or linear features extracted from an image pair. The approach described in this paper is novel in that it uses an expert system shell to develop an image correspondence knowledge-based system for the general image correspondence problem. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions in images for every edge or region matching. A computation network is used to represent this knowledge. It allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. Heuristics for determining the correspondences between image features and the problem of handling missing information will be discussed. The values of the individual matching results are used to direct the traversal and pruning of the global matching process. The problem of parallelizing the entire process will be discussed. Experimental results on real-world images show that all matching edges and regions have been identified correctly.

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Choi, Sang-Il, Yonggeol Lee e Minsik Lee. "Face Recognition in SSPP Problem Using Face Relighting Based on Coupled Bilinear Model". Sensors 19, n.º 1 (22 de dezembro de 2018): 43. http://dx.doi.org/10.3390/s19010043.

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There have been decades of research on face recognition, and the performance of many state-of-the-art face recognition algorithms under well-conditioned environments has become saturated. Accordingly, recent research efforts have focused on difficult but practical challenges. One such issue is the single sample per person (SSPP) problem, i.e., the case where only one training image of each person. While this problem is challenging because it is difficult to establish the within-class variation, working toward its solution is very practical because often only a few images of a person are available. To address the SSPP problem, we propose an efficient coupled bilinear model that generates virtual images under various illuminations using a single input image. The proposed model is inspired by the knowledge that the illuminance of an image is not sensitive to the poor quality of a subspace-based model, and it has a strong correlation to the image itself. Accordingly, a coupled bilinear model was constructed that retrieves the illuminance information from an input image. This information is then combined with the input image to estimate the texture information, from which we can generate virtual illumination conditions. The proposed method can instantly generate numerous virtual images of good quality, and these images can then be utilized to train the feature space for resolving SSPP problems. Experimental results show that the proposed method outperforms the existing algorithms.

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Elena Stoykova, Elena Stoykova, Hoonjong Kang Hoonjong Kang e Jiyung Park Jiyung Park. "Twin-image problem in digital holography—a survey (Invited Paper)". Chinese Optics Letters 12, n.º 6 (2014): 060013–60024. http://dx.doi.org/10.3788/col201412.060013.

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Kubíčková, Hana, Karel Jedlička, Radek Fiala e Daniel Beran. "Indoor Positioning Using PnP Problem on Mobile Phone Images". ISPRS International Journal of Geo-Information 9, n.º 6 (2 de junho de 2020): 368. http://dx.doi.org/10.3390/ijgi9060368.

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As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are proposing a method for an indoor positioning system using a single image. This is made possible using a small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with the SIFT (Scale Invariant Feature Transform) algorithm, we can look through the database and find an image that is the most similar to the image taken by a user. Such a pair of images is then used to find coordinates of a database of images using the PnP problem. Furthermore, projection and essential matrices are determined to calculate the user image localization—determining the position of the user in the indoor environment. The benefits of this approach lie in the single image being the only input from a user and the lack of requirements for new onsite infrastructure. Thus, our approach enables a more straightforward realization for building management.

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Heneghan, Jack. "Image Transport Quality? No Problem". SMPTE Motion Imaging Journal 111, n.º 10 (outubro de 2002): 487–94. http://dx.doi.org/10.5594/j16346.

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Ross, Alison. "The Problem of the Image". Critical Horizons 14, n.º 3 (janeiro de 2013): 355–79. http://dx.doi.org/10.1179/1440991713z.00000000015.

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Nikolay, N. Nechaev, e A. Redkina Ekaterina. "Problem of Perceptual Image Formation". Procedia - Social and Behavioral Sciences 86 (outubro de 2013): 271–76. http://dx.doi.org/10.1016/j.sbspro.2013.08.563.

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Hansford, Brian. "Teacher Education: the image problem". South Pacific Journal of Teacher Education 20, n.º 2 (janeiro de 1992): 113–20. http://dx.doi.org/10.1080/0311213920200204.

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Bulygin, M. V., M. M. Gayanova, A. M. Vulfin, A. D. Kirillova e R. Ch Gayanov. "Convolutional neural network in the images colorization problem". Information Technology and Nanotechnology, n.º 2416 (2019): 340–53. http://dx.doi.org/10.18287/1613-0073-2019-2416-340-353.

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Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.

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Wheatley, Grayson H., e Anne M. Reynolds. "Image Maker: Developing Spatial Sense". Teaching Children Mathematics 5, n.º 6 (fevereiro de 1999): 374–78. http://dx.doi.org/10.5951/tcm.5.6.0374.

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Just as it is useful to have a mental map of streets in a city, it is also useful to have mental images of mathematical patterns and relationships. A student who has constructed a network of image-based mathematical meanings can more easily devise solutions to problems. If a student knows only one way and if the task is not straightforward, he or she may not be able to solve the problem. The ability to construct and transform mental images leads to flexibility and power. In doing mathematics, it is advantageous to know more than one way to solve a problem or complete a routine task. Mental images play a crucial role in all aspects of mathematics.

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Chochia, P. A. "Two–Scale Image Analysis in the Image Smoothing Problem". Procedia Engineering 201 (2017): 223–30. http://dx.doi.org/10.1016/j.proeng.2017.09.619.

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Kovalchuk, A. M. "BINARY LINEAR TRANSFORMATIONS IN MODIFICATIONS OF RSA ALGORITHM OF IMAGES". Ukrainian Journal of Information Technology 2, n.º 1 (2020): 37–42. http://dx.doi.org/10.23939/ujit2020.02.037.

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The images are one of the most used kinds of the information in modern information company. Therefore actual problems is the organization of protection from unauthorized access and usage. An important characteristic of the image is the presence of contours in the image. The task of contour selection requires the use of operations on adjacent elements that are sensitive to change and suppress areas of constant levels of brightness, that is, contours are those areas where changes occur, becoming light, while other parts of the image remain dark. Mathematically, the ideal outline is to break the spatial function of the brightness levels in the image plane. Therefore, contour selection means finding the most dramatic changes, that is, the maxima of the gradient vector module. This is one of the reasons that the contours remain in the image when encrypted in the RSA system, since the encryption here is based on a modular elevation of some natural number. At the same time, on the contour and on the neighboring contours of the peak villages, the elevation of the brightness value gives an even bigger gap. Problem protect from unauthorized access is by more composite in matching with a problem protect from usage. Basis for organization of protection is the interpretation of the image as stochastic signal. It stipulates carry of methods of encoding of signals on a case of the images. But the images are a specific signal, which one in possesses, is padding to representative selfless creativeness, also by visual selfless creativeness. Therefore to methods of encoding, in case of their usage concerning the images, one more requirement – full noise of the coded image is put forward. It is necessary to make to impossible usage of methods of visual image processing. The algorithm RSA is one of the most used production specifications of encoding of signals. In attitude of the images there are some problems of its encoding, the contours on the coded image are in particular saved. Therefore actual problem is the mining of modification to a method RSA such, that: to supply stability to decoding; to supply full noise of the images. One of pathes of the solution of this problem is usage of affine transformations.

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Vachkov, I. V., e M. A. Sukhoruchenkov. "Special Aspects of Sensual Images During Imago Therapy Process". Клиническая и специальная психология 6, n.º 4 (2017): 125–47. http://dx.doi.org/10.17759/cpse.2017060409.

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The article presents the results of the study performed on 27 adults who have completed five imago therapeutic sessions. The subjects were split in two groups: problem-solving group and problem-analysis group. The peculiarities of sensory images that arise during the sessions by Glezer grounded theory method were studied. It turned out that the subject sensual tissue of the psychosemiological tetrahedron (the term of F. Vasiljuk) of the imago therapeutic image is represented mainly by sensory imaginary events and less frequently by sensual objects and recalled events. The pole and sensory tissue of meaning are least expressed in the imago therapeutic image. The pole and sensory tissue of personal meaning are significantly expressed by the imago therapeutic image. Not only elements relating to the imagination are woven into the imago therapeutic image but also elements reflecting the process of imaginative psychotherapy as a whole, a separate session, the process of imagination. These elements are closely related to the elements of imaginary and remembered events, their feelings and comprehension.

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Erfanian, A. "A problem on growth sequences of groups". Journal of the Australian Mathematical Society. Series A. Pure Mathematics and Statistics 59, n.º 2 (outubro de 1995): 283–86. http://dx.doi.org/10.1017/s1446788700038684.

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AbstractThe aim of this paper is to consider Problem 1 posed by Stewart and Wiegold in [6]. The main result is that if G is a finitely generated perfect group having non-trivial finite images, then there exists a finite image B of G such that the growth sequence of B is eventuallly faster than that of every finite image of G. Moreover we investigate the growth sequences of simple groups of the same order.

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Ahmad Khan, Mushtaq, Zawar Hussain Khan, Haseeb khan, Sheraz Khan e Suhail Khan. "Collocation Method for Multiplicative Noise Removal Model". Mehran University Research Journal of Engineering and Technology 39, n.º 4 (1 de outubro de 2020): 734–43. http://dx.doi.org/10.22581/muet1982.2004.05.

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Image denoising is a fundamental problem in both image processing and computer vision with numerous applications. It can be formulated as an inverse problem. Variational methods are commonly used to solve noise removal problems. The Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with multiplicative noise into a more general technique for inverse problems such as denoising, deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. Multiplicative noise removal based on TV regularization has been widely researched in image science. In multiplicative noise problems, original image is multiplied by a noise rather than added to the original image. This article proposes a novel meshless collocation technique for the solution of a model having multiplicative noise. This technique includes TV and local collocation along with Multiquadric Radial Basis Function (MQ-RBF) for the solution of associated Euler-Lagrange equation for restoring multiplicative noise from digital images. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details while the noise in the homogeneous regions is removed sufficiently. As a consequence, our method yields better denoised results than those of the current state of the art methods with respect to the Peak-Signal to Noise Ratio (PSNR) values.

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Chen, Yanqiao, Yangyang Li, Licheng Jiao, Cheng Peng, Xiangrong Zhang e Ronghua Shang. "Adversarial Reconstruction-Classification Networks for PolSAR Image Classification". Remote Sensing 11, n.º 4 (18 de fevereiro de 2019): 415. http://dx.doi.org/10.3390/rs11040415.

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Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more widely used in recent years. It is well known that PolSAR image classification is a dense prediction problem. The recently proposed fully convolutional networks (FCN) model, which is very good at dealing with the dense prediction problem, has great potential in resolving the task of PolSAR image classification. Nevertheless, for FCN, there are some problems to solve in PolSAR image classification. Fortunately, Li et al. proposed the sliding window fully convolutional networks (SFCN) model to tackle the problems of FCN in PolSAR image classification. However, only when the labeled training sample is sufficient, can SFCN achieve good classification results. To address the above mentioned problem, we propose adversarial reconstruction-classification networks (ARCN), which is based on SFCN and introduces reconstruction-classification networks (RCN) and adversarial training. The merit of our method is threefold: (i) A single composite representation that encodes information for supervised image classification and unsupervised image reconstruction can be constructed; (ii) By introducing adversarial training, the higher-order inconsistencies between the true image and reconstructed image can be detected and revised. Our method can achieve impressive performance in PolSAR image classification with fewer labeled training samples. We have validated its performance by comparing it against several state-of-the-art methods. Experimental results obtained by classifying three PolSAR images demonstrate the efficiency of the proposed method.

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Williams, Bryan M., Jianping Zhang e Ke Chen. "A new image deconvolution method with fractional regularisation". Journal of Algorithms & Computational Technology 10, n.º 4 (28 de julho de 2016): 265–76. http://dx.doi.org/10.1177/1748301816660439.

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Image deconvolution is an important pre-processing step in image analysis which may be combined with denoising, also an important image restoration technique, and prepares the image to facilitate diagnosis in the case of medical images and further processing such as segmentation and registration. Considering the variational approach to this problem, regularisation is a vital component for reconstructing meaningful information and the problem of defining appropriate regularisation is an active research area. An important question in image deconvolution is how to obtain a restored image which has sharp edges where required but also allows smooth regions. Many of the existing regularisation methods allow for one or the other but struggle to obtain good results with both. Consequently, there has been much work in the area of variational image reconstruction in finding regularisation techniques which can provide good quality restoration for images which have both smooth regions and sharp edges. In this paper, we propose a new regularisation technique for image reconstruction in the blind and non-blind deconvolution problems where the precise cause of blur may or may not be known. We present experimental results which demonstrate that this method of regularisation is beneficial for restoring images and blur functions which contain both jumps in intensity and smooth regions.

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Jia, Zhaoyang, e Guangxue Chen. "Study on Digital Image Inpainting Method Based on Multispectral Image Decomposition Synthesis". International Journal of Pattern Recognition and Artificial Intelligence 33, n.º 01 (11 de outubro de 2018): 1954004. http://dx.doi.org/10.1142/s0218001419540041.

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The paper analyzes the image inpainting problem of damaged Painting Arts for high fidelity images reproduction, and a digital image inpainting method based on multispectral image decomposition synthesis is proposed. Firstly, multi-channel images of Painting Arts are obtained by multispectral technology. Then, a polynomial regression method based on principal component is used to reconstruct the spectral image. The reconstructed image is decomposed by VO image decomposition model. During the inpainting process, the channel correlation of the structure image and the texture image of multispectral image is effectively removed. The digital image inpainting is performed respectively. Finally, the digital inpainted image is obtained by synthesis. The experimental results show that the digital image inpainting based on multispectral image decomposition synthesis reduces the problem of low image inpainting accuracy caused by the correlation between the color components in the traditional digital image inpainting process, and reduces the mismatch of the inpainting image. Appearance of pseudo color of inpainting image is reduced. MSE of multispectral images inpainting qualities is 2.7951 and PSNR of multispectral images inpainting qualities is 44.1681, so it is superior to traditional image inpainting algorithm. It provides a reliable basis for digital inpainting, digital archives and high fidelity replication of defective Painting Arts.

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Wang, Xian Qiu, Xiu Bi Wang e Xiao Li Huang. "Image Segmentation Based on Wavelet Transform". Advanced Materials Research 225-226 (abril de 2011): 1041–44. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.1041.

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Image segmentation is one of the fundamental problems in image processing and computer vision. Studies of high quality image segmentation methods have always gained a lot of attention in the field of image processing. However，so far the problem of image segmentation has not been well solved yet. Conventional methods cannot divide the images exactly because too much objectivity has been used. For complex objects, this paper proposed an efficient image segmentation algorithm based wavelet transform. This article presents the result of wavelet image segmentation and watershed algorithm image segmentation. The experimental result indicates that, the algorithm based on wavelet transform has fast convergence and good noise immunity.

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Neal, Brent. "Photoshop and 12-bit Digital Microscope Camera Images". Microscopy Today 12, n.º 2 (março de 2004): 24–25. http://dx.doi.org/10.1017/s1551929500051956.

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One problem facing owners of high-end digital microscope cameras and scanners is dealing with 12-bit TIFF format images. Because of a vagueness in the TIFF specification [1], some prograins do not load 12-bit TIFF images at all, or do not handle them gracefully. Unfortunately, Adobe Photoshop is one such program.A common problem with 12-bit images that do load in Photoshop is that the image appears to be totally black once loaded. This occurs when the camera stores the most significant bits of the image in the lower 12-bits of a 16-bit data space. Photoshop, when converting to its internal 16-bit representation, does not scale these properly, resulting in a low contrast image. This can present problems with viewing the images, or performing any processing or measurement steps that you might desire.

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Chen, Wei, Kohei Inoue e Kenji Hara. "Adaptive Aggregated Histogram Equalization for Color Image Enhancement without Gamut Problem". Journal of the Institute of Industrial Applications Engineers 8, n.º 2 (25 de abril de 2020): 56–62. http://dx.doi.org/10.12792/jiiae.8.56.

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AMADORI, A. L., e J. L. VÁZQUEZ. "SINGULAR FREE BOUNDARY PROBLEM FROM IMAGE PROCESSING". Mathematical Models and Methods in Applied Sciences 15, n.º 05 (maio de 2005): 689–715. http://dx.doi.org/10.1142/s0218202505000509.

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We study a degenerate nonlinear parabolic equation with moving boundaries which arises in the study of the technique of contour enhancement in image processing. In order to obtain mass concentration at the contour, singular data are imposed at the free boundary, leading to a nonstandard free boundary problem. Our main results are: (i) the well-posedness for the singular problem, without monotonicity assumptions on the initial datum, and (ii) the convergence of the approximation by means of combustion-type free-boundary problems.

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Jaafar Belaid, Lamia. "An Overview of the Topological Gradient Approach in Image Processing: Advantages and Inconveniences". Journal of Applied Mathematics 2010 (2010): 1–19. http://dx.doi.org/10.1155/2010/761783.

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Image analysis by topological gradient approach is a technique based upon the historic application of the topological asymptotic expansion to crack localization problem from boundary measurements. This paper aims at reviewing this methodology through various applications in image processing; in particular image restoration with edge detection, classification and segmentation problems for both grey level and color images is presented in this work. The numerical experiments show the efficiency of the topological gradient approach for modelling and solving different image analysis problems. However, the topological gradient approach presents a major drawback: the identified edges are not connected and then the results obtained particularly for the segmentation problem can be degraded. To overcome this inconvenience, we propose an alternative solution by combining the topological gradient approach with the watershed technique. The numerical results obtained using the coupled method are very interesting.

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Nakatani, Hiromasa. "Boundary value problem of image modification". Optical Engineering 31, n.º 2 (1992): 280. http://dx.doi.org/10.1117/12.56069.

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Harris, John. "The Image Problem in Women’s Football". Journal of Sport and Social Issues 29, n.º 2 (maio de 2005): 184–97. http://dx.doi.org/10.1177/0193723504273120.

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Chalendar, Isabelle, e Jonathan R. Partington. "An image problem for compact operators". Proceedings of the American Mathematical Society 134, n.º 05 (7 de outubro de 2005): 1391–96. http://dx.doi.org/10.1090/s0002-9939-05-08084-6.

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Gavaghan, Helen. "Physics grapples with its image problem". Nature 398, n.º 6724 (março de 1999): 265–66. http://dx.doi.org/10.1038/18482.

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Kleinwort, Ellie. "Body Love: Society's Body Image Problem". Museums & Social Issues 6, n.º 2 (setembro de 2011): 174–81. http://dx.doi.org/10.1179/msi.2011.6.2.174.

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Lee, Chia-Hoang, e Anupam Joshi. "Correspondence problem in image sequence analysis". Pattern Recognition 26, n.º 1 (janeiro de 1993): 47–61. http://dx.doi.org/10.1016/0031-3203(93)90087-d.

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Dieringer, N., e W. J. Daunicht. "Image fading ? a problem for frogs?" Naturwissenschaften 73, n.º 6 (junho de 1986): 330–32. http://dx.doi.org/10.1007/bf00451484.

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Dvinina, S. Y. "UNIVERSITY DIGITAL IMAGE: DEFINING THE PROBLEM". Sign: problematic field in mediaeducation, n.º 3 (2021): 7–12. http://dx.doi.org/10.47475/2070-0695-2021-10301.

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Birkin, Jane. "Institutional Metadata and the Problem of Context". Digital Culture & Society 6, n.º 2 (1 de dezembro de 2020): 17–34. http://dx.doi.org/10.14361/dcs-2020-0203.

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Abstract The traditional archive catalogue constitutes a form of structural and descriptive metadata that long precedes the internet; and the cataloguing of photographs is just one part of a process of archival administration. The application of keywords to images contrasts with archival prose description, which is based on the visual content of the image and is predominantly context-free; a remediation of the image itself. At the heart of this lies the notion that the single photograph is itself devoid of context; it is a discrete embodiment of shutter time and there is nothing certain either side of that. Thus, one can only speculate at its context, and institutional description techniques actively avoid such speculation. Yet context in the archive is ever-present and key to the function of images as objects of information and evidence. It is built through static relationships, through the situating of photographs in accordance with the concept of original order, and it is replicated through storage systems and hierarchical catalogue entries. Such orders, hierarchies and relationships are absent within sets of images that are brought together by keyword search, including through the websites of archival institutions that struggle to reconcile archival principles and identity with network culture. Images are transported to places where contextual information is at best difficult to access, especially for those unfamiliar with archival interfaces. In contrast to the controlled stasis of archival storage and interconnected recordkeeping systems, network storage is messy, unstable and poorly described. However, we must accept that context is not a prerequisite for many users, and for them the networking of archival images denotes a freedom; a democratisation of the archive. But in a media-driven society that is becoming more and more indifferent to the evidential value of documents of any kind, the context-free image is left predisposed to exploitation.

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Su, Ching Hung, Huang Sen Chiu, Mohd Helmy A. Wahab, Tsai Ming Hsiehb, You Chiuan Li e Jhao Hong Lin. "Images Retrieval Based on Integrated Features". Applied Mechanics and Materials 543-547 (março de 2014): 2292–95. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2292.

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We propose a practical image retrieval scheme to retrieve images efficiently. The proposed scheme transfers each image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the images, namely color sequences comparison. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Edge Histogram Descriptor to compare the images of database. We succeed in transferring the image retrieval problem to quantized code comparison. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues both of the content based image retrieval system and a text based image retrieval system.

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