1,113 research outputs found

    Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization

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    Convex optimization with sparsity-promoting convex regularization is a standard approach for estimating sparse signals in noise. In order to promote sparsity more strongly than convex regularization, it is also standard practice to employ non-convex optimization. In this paper, we take a third approach. We utilize a non-convex regularization term chosen such that the total cost function (consisting of data consistency and regularization terms) is convex. Therefore, sparsity is more strongly promoted than in the standard convex formulation, but without sacrificing the attractive aspects of convex optimization (unique minimum, robust algorithms, etc.). We use this idea to improve the recently developed 'overlapping group shrinkage' (OGS) algorithm for the denoising of group-sparse signals. The algorithm is applied to the problem of speech enhancement with favorable results in terms of both SNR and perceptual quality.Comment: 14 pages, 11 figure

    The Perception-Distortion Tradeoff

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    Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this paper, we prove mathematically that distortion and perceptual quality are at odds with each other. Specifically, we study the optimal probability for correctly discriminating the outputs of an image restoration algorithm from real images. We show that as the mean distortion decreases, this probability must increase (indicating worse perceptual quality). As opposed to the common belief, this result holds true for any distortion measure, and is not only a problem of the PSNR or SSIM criteria. We also show that generative-adversarial-nets (GANs) provide a principled way to approach the perception-distortion bound. This constitutes theoretical support to their observed success in low-level vision tasks. Based on our analysis, we propose a new methodology for evaluating image restoration methods, and use it to perform an extensive comparison between recent super-resolution algorithms.Comment: CVPR 2018 (long oral presentation), see talk at: https://youtu.be/_aXbGqdEkjk?t=39m43

    BAYESIAN ENSEMBLE LEARNING FOR MEDICAL IMAGE DENOISING

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    Medical images are often affected by random noise because of both image acquisition from the medical modalities and image transmission from modalities to workspace in the main computer. Medical image denoising removes noise from the CT or MR images and it is an essential step that makes diagnosing more efficient. Many denoising algorithms have been introduced such as Non-local Means, Fields of Experts, and BM3D. In this thesis, we implement the Bayesian ensemble learning for not only natural image denoising but also medical image denoising. The Bayesian ensemble models are Non-local Means and Fields of Experts, the very successful recent algorithms. The Non-local Means presumes that the image contains an extensive amount of self-similarity. The approach of the Fields of Experts model extends traditional Markov Random Field model by learning potential functions over extended pixel neighborhoods. The two models are implemented, and image denoising is performed on both natural images and MR images. For MR images, we used two noise distributions, Gaussian and Rician. The experimental results obtained are used to compare with the single algorithm, and discuss the ensemble learning and their approaches

    Sparse image reconstruction on the sphere: implications of a new sampling theorem

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    We study the impact of sampling theorems on the fidelity of sparse image reconstruction on the sphere. We discuss how a reduction in the number of samples required to represent all information content of a band-limited signal acts to improve the fidelity of sparse image reconstruction, through both the dimensionality and sparsity of signals. To demonstrate this result we consider a simple inpainting problem on the sphere and consider images sparse in the magnitude of their gradient. We develop a framework for total variation (TV) inpainting on the sphere, including fast methods to render the inpainting problem computationally feasible at high-resolution. Recently a new sampling theorem on the sphere was developed, reducing the required number of samples by a factor of two for equiangular sampling schemes. Through numerical simulations we verify the enhanced fidelity of sparse image reconstruction due to the more efficient sampling of the sphere provided by the new sampling theorem.Comment: 11 pages, 5 figure

    Advanced Techniques for Ground Penetrating Radar Imaging

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    Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

    Recent Progress in Image Deblurring

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    This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

    Comparative Analysis of MFO, GWO and GSO for Classification of Covid-19 Chest X-Ray Images

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    تلعب الصور الطبية دورًا حاسمًا في تصنيف الأمراض والحالات المختلفة. إحدى طرق التصوير هي الأشعة السينية التي توفر معلومات بصرية قيمة تساعد في تحديد وتوصيف مختلف الحالات الطبية. لطالما استخدمت الصور الشعاعية للصدر (CXR) لفحص ومراقبة العديد من اضطرابات الرئة، مثل السل والالتهاب الرئوي وانخماص الرئة والفتق. يمكن الكشف عن COVID-19 باستخدام صور CXR أيضًا. تم اكتشاف COVID-19، وهو فيروس يسبب التهابات في الرئتين والممرات الهوائية في الجهاز التنفسي العلوي، لأول مرة في عام 2019 في مقاطعة ووهان بالصين، ومنذ ذلك الحين يُعتقد أنه يتسبب في تلف كبير في مجرى الهواء، مما يؤثر بشدة على رئة الأشخاص المصابين. انتشر الفيروس بسرعة في جميع أنحاء العالم، وتم تسجيل الكثير من الوفيات والحالات المتزايدة بشكل يومي. يمكن استخدام CXR لمراقبة آثار COVID-19 على أنسجة الرئة. تبحث هذه الدراسة في تحليل مقارنة لأقرب جيران k (KNN)، و Extreme Gradient Boosting (XGboost)، و Support-Vector Machine (SVM)، وهي بعض مناهج التصنيف لاختيار الميزات في هذا المجال باستخدام خوارزمية Moth-Flame Optimization (MFO)، وخوارزمية Gray Wolf Optimizer (GWO)، وخوارزمية Glowworm Swarm Optimization (GSO). في هذه الدراسة، استخدم الباحثون مجموعة بيانات تتكون من مجموعتين على النحو التالي: 9544 صورة بالأشعة السينية ثنائية الأبعاد، والتي تم تصنيفها إلى مجموعتين باستخدام اختبارات التحقق من صحتها: 5500 صورة لرئتين سليمتين و4044 صورة للرئتين مع COVID-19. تتضمن المجموعة الثانية 800 صورة و400 صورة لرئتين سليمتين و400 رئة مصابة بـ COVID-19. تم تغيير حجم كل صورة إلى 200 × 200 بكسل. كانت الدقة والاستدعاء ودرجة F1 من بين معايير التقييم الكمي المستخدمة في هذه الدراسة.Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons. The virus was swiftly gone viral around the world and a lot of fatalities and cases growing were recorded on a daily basis. CXR can be used to monitor the effects of COVID-19 on lung tissue. This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). For this study, researchers employed a data set consisting of two sets as follows: 9,544 2D X-ray images, which were classified into two sets utilizing validated tests: 5,500 images of healthy lungs and 4,044 images of lungs with COVID-19. The second set includes 800 images, 400 of healthy lungs and 400 of lungs affected with COVID-19. Each image has been resized to 200x200 pixels. Precision, recall, and the F1-score were among the quantitative evaluation criteria used in this study
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