Image denoising. For image denoising, Zhang et al. [2] developed a convolutional neural network, named DnCNN, using common components such as convolution, batch normalization, and rectified linear unit layers. Chang et al. [61] introduced a spatial-adaptive denoising network that efficiently removes image noise. By utilizing fractional optimal control …
Most existing super-resolution (SR) methods assume that the degradation is fixed (e.g., bicubic downsampling), whereas their performance would be degraded if the actual degradation differs from this assumption. To deal with unknown degradations, existing blind SR methods are committed to learning degradation representation to generate high-resolution …
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Image restoration in digital image processing is a specialized technique aimed at reversing these impairments, restoring images to their original or improved quality. This process not only ensures the preservation of visual data but also enhances its utility across numerous fields like healthcare, astronomy, forensics, and entertainment.
Image fusion, a fundamental low-level vision task, aims to integrate multiple image sequences into a single output while preserving as much information as possible from the input. However, existing methods face several significant limitations: 1) requiring task- or dataset-specific models; 2) neglecting real-world image degradations (textit{e.g.}, noise), which …
Design a new degradation model to synthesize LR images for training: 1) Make the blur, downsampling and noise more practical. Blur: two convolutions with isotropic and anisotropic Gaussian kernels from both the HR space and LR space Downsampling: nearest, bilinear, bicubic, down-up-sampling Noise: Gaussian noise, JPEG compression noise, processed …
Digital images make it possible to record and display helpful information; however, they are vulnerable to defects or degradation during image acquisition [].One of the most common defects is blurring (a form of bandwidth compression of the original digital image) owing to the defective image formation process [].Blurring can be caused by movement between the …
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Hyperspectral image (HSI) fusion is an efficient technique that combines low-resolution HSI (LR-HSI) and high-resolution multispectral images (HR-MSI) to generate high-resolution HSI (HR-HSI). Existing supervised learning methods (SLMs) can yield promising results when test data degradation matches the training ones, but they face challenges in …
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Traditional single-task image restoration methods excel in handling specific degradation types but struggle with multiple degradations. To address this limitation, we propose Grouped Restoration with Image Degradation Similarity (GRIDS), a novel approach that harmonizes the competing objectives inherent in multiple-degradation restoration. We first …
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Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method to quantify changes in image deep features under varying degradation conditions. Specifically, our approach facilitates flexible and adaptive degradation, enabling ...
La gradation est une figure de style qui consiste à énumérer des mots ou groupes de mots qui évoquent une idée similaire avec une intensité croissante ou décroissante. Cette figure permet de créer un effet d'amplification de par la répétition d'une même idée avec une force différente. Le sens de la phrase s'en r…
Les figures de style sont des images que les auteurs utilisent pour donner plus de force à ce qu'ils expriment, ou plus de couleurs. Il en existe de multiples : voici les plus courantes d'entre elles. …
The radiometric reconstruction of an image, also called restoration [], indicates a set of techniques that perform quantitative corrections on the image to compensate for the degradations introduced during the acquisition and transmission process.These degradations are represented by the fog or blurring effect caused by the optical system and by the motion of the …
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Image upgradation requires a complicated balance between high-level contextualised information and spatial specific details. Existing approaches to solving the problems are designed to focus on single corruption, which unavoidably results in poor performance when the acquisitions suffer from multiple degradations. ... For medical imaging …
estimate f(x, y) of the original image. the estimate should be as close as possible to the original input image and, in general, the more we know about H and η, the closer f(x, y) will be to f(x, y). The degraded image is given in the spatial domain by g (x, y) = h (x, y) * f (x, y) + η (x, y)
La gradation est une figure de style qui consiste à donner de l'intensité à un discours en organisant les termes d'une phrase ou d'un texte selon un ordre d'importance …
Reconstructing missing details from degraded low-quality inputs poses a significant challenge. Recent progress in image restoration has demonstrated the efficacy of learning large models capable of addressing various degradations simultaneously. Nonetheless, these approaches introduce considerable computational overhead and complex learning …
Unlike previous methods [22, 23], in this paper, we propose learning a neural degradation representation (NDR) that effectively captures the essential characteristics of various degradations.NDR is a learnable tensor, initialized randomly and optimized adaptively through the training process. By leveraging NDR, our proposed all-in-one image restoration network, …
Image restoration technology can be divided into two categories: unconstrained restoration and constrained restoration (Gonzalez and Woods 2008).Image restoration technology can also be divided into two categories, automatic and interactive, depending on whether external intervention is required (Zhang 2009).There are many corresponding technologies for various …
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where * denotes the convolution of a Gaussian function G(r, t) of standard deviation t with x(r), the initial data.The solution specifies that the time evolution in (12.12) is a convolution process performing Gaussian smoothing. However, as the time evolution iteration progresses, the function y(r, t) becomes the product of the convolution of the input image with a Gaussian of constantly ...
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The mathematical model-based approaches are the predominant methods in LLIE tasks before the emergence of data-driven methods, which can be broadly categorized as Histogram Equalization (HE)-based methods and Retinex model-based methods. HE-based methods [19], [20] enhanced low-light images by redistributing the distribution of images' pixel …
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Restoring images captured under adverse weather conditions is a fundamental task for many computer vision applications. However, most existing weather restoration approaches are only capable of handling a specific type of degradation, which is often insufficient in real-world scenarios, such as rainy-snowy or rainy-hazy weather. Towards being able to …
Abstract page for arXiv paper 2412.20157: UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity. Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are limited in...
Image restoration and enhancement are pivotal for numerous computer vision applications, yet unifying these tasks efficiently remains a significant challenge. Inspired by the iterative refinement capabilities of diffusion models, we propose CycleRDM, a novel framework designed to unify restoration and enhancement tasks while achieving high-quality mapping. …
Image 1 with a high-quality image without DPR and Image 2 with a low-quality image with DPR optimization. NB: The dpr parameter can only be used when either the height or width of the desired output image is specified. Conclusion.