Adaptive Non-linear Filtering Technique for Image Restoration
Rungta College of Engineering & Technology（龙塔工程技术学院）；National Institute of Science and Technology (NIST)（国家科学技术学院（NIST））；Aditya Institute of Technology（阿迪蒂亚理工学院）；National Academy of Aviation（国家航空学院）
S. K. Satpathy, S. Panda, K. K. Nagwanshi, S. K. Nayak, C. Ardil
备注：
Accepted. arXiv admin note: text overlap with arXiv:1003.1827 by other authors
发表信息：
World Academy of Science, Engineering and Technology, 68, 352-359 (2010)
Removing noise from the any processed images is very important. Noise should be removed in such a way that important information of image should be preserved. A decisionbased nonlinear algorithm for elimination of band lines, drop lines, mark, band lost and impulses in images is presented in this paper. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and evaluation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. However, the restricted window size renders median operation less effective whenever noise is excessive in that case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of Mean Square Error [MSE], Peak-Signal-to-Noise Ratio [PSNR], Signal-to-Noise Ratio Improved [SNRI], Percentage Of Noise Attenuated [PONA], and Percentage Of Spoiled Pixels [POSP]. This is compared with standard algorithms already in use and improved performance of the proposed algorithm is presented. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms which are required for removal of different artifacts.
Centre for Intelligent Sensing, Queen Mary University of London（伦敦玛丽女王大学智能感知中心）
Underwater images are degraded by the selective attenuation of light that distorts colours and reduces contrast. The degradation extent depends on the water type, the distance between an object and the camera, and the depth under the water surface the object is at. Underwater image filtering aims to restore or to enhance the appearance of objects captured in an underwater image. Restoration methods compensate for the actual degradation, whereas enhancement methods improve either the perceived image quality or the performance of computer vision algorithms. The growing interest in underwater ima…
Rungta College of Engineering & Technology（龙格塔工程技术学院）；National Institute of Science and Technology (NIST)（国家科学技术学院（NIST））；National Academy of Aviation（国家航空学院）
This paper proposes a new technique based on a non-linear Minmax Detector Based (MDB) filter for image restoration. The aim of image enhancement is to reconstruct the true image from the corrupted image. The process of image acquisition frequently leads to degradation and the quality of the digitized image becomes inferior to the original image. Image degradation can be due to the addition of different types of noise in the original image. Image noise can be modelled of many types and impulse noise is one of them. Impulse noise generates pixels with gray value not consistent with their local n…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；The Chinese University of Hong Kong（香港中文大学）
In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of few additional par…
Small compression noises, despite being transparent to human eyes, can adversely affect the results of many image restoration processes, if left unaccounted for. Especially, compression noises are highly detrimental to inverse operators of high-boosting (sharpening) nature, such as deblurring and superresolution against a convolution kernel. By incorporating the non-linear DCT quantization mechanism into the formulation for image restoration, we propose a new sparsity-based convex programming approach for joint compression noise removal and image restoration. Experimental results demonstrate s…
Learning Adaptive Dynamical Features via Multi-$τ$ Liquid-Mamba for All-in-one Image Restoration
Department of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学与技术系）；Department of Architecture and Design, Harbin Institute of Technology（哈尔滨工业大学建筑与设计系）
Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of Science and Technology of China（中国科学技术大学）
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance
Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究所）；Biomedical Engineering Department, College of Future Technology, Peking University（未来技术学院生物医学工程系）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）；China Mobile Research Institute（中国移动研究院）
Harbin Institute of Technology（哈尔滨工业大学）；Sun Yat-Sen University（中山大学）
The tradeoff between receptive field size and efficiency is a crucial issue in low level vision. Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of computational cost. Recently, dilated filtering has been adopted to address this issue. But it suffers from gridding effect, and the resulting receptive field is only a sparse sampling of input image with checkerboard patterns. In this paper, we present a novel multi-level wavelet CNN (MWCNN) model for better tradeoff between receptive field size and computational efficiency. With the modified U-Net architec…
AdaQual-Diff: Diffusion-Based Image Restoration via Adaptive Quality Prompting
Fuzhou University（福州大学）；University of Science and Technology of China（中国科学技术大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Shandong Normal University（山东师范大学）；Sun Yat-sen University（中山大学）
The Chinese University of Hong Kong（香港中文大学）；SenseTime Research（商汤科技研究院）
How to aggregate spatial information plays an essential role in learning-based image restoration. Most existing CNN-based networks adopt static convolutional kernels to encode spatial information, which cannot aggregate spatial information adaptively. Recent transformer-based architectures achieve adaptive spatial aggregation. But they lack desirable inductive biases of convolutions and require heavy computational costs. In this paper, we propose a kernel basis attention (KBA) module, which introduces learnable kernel bases to model representative image patterns for spatial information aggrega…
Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model. We present an approach that is highly accurate and allows a significant reduction in the number of parameters. In contrast to existing methods, our approach can restore images using a single fixed-size model, regardless of the number of degradation levels. On popular datasets, our approach yields state-of-the-art results in terms of size and accuracy for a variety of image restoration tasks, including denoising, deJPEG, and super-resolution.
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation
Technical University of Munich（慕尼黑工业大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）；Mohammed Bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；University of Central Florida（中佛罗里达大学）；Linköping University（林雪平大学）
The triangulation of images has become an active research area in recent years for its compressive representation and ease of image processing and visualization. However, little work has been done on how to faithfully recover image intensities from a triangulated mesh of an image, a process also known as image restoration or decoding from meshes. The existing methods such as linear interpolation, least-square interpolation, or interpolation based on radial basis functions (RBFs) work to some extent, but often yield blurred features (edges, corners, etc.). The main reason for this problem is du…
All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model
School of Computing, Australian National University（澳大利亚国立大学计算学院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）
Tohoku University（东北大学）；RIKEN Center for AIP（理化学研究所AIP中心）
Many studies have been conducted so far on image restoration, the problem of restoring a clean image from its distorted version. There are many different types of distortion which affect image quality. Previous studies have focused on single types of distortion, proposing methods for removing them. However, image quality degrades due to multiple factors in the real world. Thus, depending on applications, e.g., vision for autonomous cars or surveillance cameras, we need to be able to deal with multiple combined distortions with unknown mixture ratios. For this purpose, we propose a simple yet e…
This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.
Xi’an University of Science and Technology（西安科技大学）
A novel color image enhancement method is proposed based on Retinex to enhance color images under non-uniform illumination or poor visibility conditions. Different from the conventional Retinex algorithms, the Weighted Guided Image Filter is used as a surround function instead of the Gaussian filter to estimate the background illumination, which can overcome the drawbacks of local blur and halo artifact that may appear by Gaussian filter. To avoid color distortion, the image is converted to the HSI color model, and only the intensity channel is enhanced. Then a linear color restoration algorit…
Degradation-Aware Adaptive Context Gating for Unified Image Restoration
School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算机与人工智能学院，西南交通大学）；Institute for Infocomm Research, Agency for Science, Technology and Research（信息通信研究院，科技研究局）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（计算机与人工智能学院，江西财经大学）；School of Information Management, Jiangxi University of Finance and Economics（信息管理学院，江西财经大学）；Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education（可持续智慧城市交通工程研究中心，教育部）；National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University（综合交通运输大数据应用技术国家工程实验室，西南交通大学）
PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One Remote Sensing Image Restoration
School of Electronic and Information Engineering, Harbin Institute of Technology（电子与信息工程学院，哈尔滨工业大学）；Heilongjiang Province Key Laboratory of Space-Air-Ground Integrated Intelligent Remote Sensing（黑龙江省空间-空天地一体化智能遥感重点实验室）
While Transformer has achieved remarkable performance in various high-level vision tasks, it is still challenging to exploit the full potential of Transformer in image restoration. The crux lies in the limited depth of applying Transformer in the typical encoder-decoder framework for image restoration, resulting from heavy self-attention computation load and inefficient communications across different depth (scales) of layers. In this paper, we present a deep and effective Transformer-based network for image restoration, termed as U2-Former, which is able to employ Transformer as the core oper…
Deep convolutional networks have attracted great attention in image restoration and enhancement. Generally, restoration quality has been improved by building more and more convolutional block. However, these methods mostly learn a specific model to handle all images and ignore difficulty diversity. In other words, an area in the image with high frequency tend to lose more information during compressing while an area with low frequency tends to lose less. In this article, we adrress the efficiency issue in image SR by incorporating a patch-wise rolling network(PRN) to content-adaptively recover…
Princeton University（普林斯顿大学）；University of Central Florida（中佛罗里达大学）
The problem of image restoration in cryo-EM entails correcting for the effects of the Contrast Transfer Function (CTF) and noise. Popular methods for image restoration include `phase flipping', which corrects only for the Fourier phases but not amplitudes, and Wiener filtering, which requires the spectral signal to noise ratio. We propose a new image restoration method which we call `Covariance Wiener Filtering' (CWF). In CWF, the covariance matrix of the projection images is used within the classical Wiener filtering framework for solving the image restoration deconvolution problem. Our estim…
ATD: Improved Transformer with Adaptive Token Dictionary for Image Restoration
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC)（计算机科学与工程学院（网络安全学院），电子科学与技术大学（UESTC））
Institute of Optics and Electronics, Chinese Academy of Sciences（中国科学院光电技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Image quality is the basis of image communication and understanding tasks. Due to the blur and noise effects caused by imaging, transmission and other processes, the image quality is degraded. Blind image restoration is widely used to improve image quality, where the main goal is to faithfully estimate the blur kernel and the latent sharp image. In this study, based on experimental observation and research, an adaptively sparse regularized minimization method is originally proposed. The high-order gradients combine with low-order ones to form a hybrid regularization term, and an adaptive opera…
Technische Universität Dresden（德累斯顿工业大学）；Center for Interdisciplinary Digital Sciences (CIDS)（跨学科数字科学中心）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig（德累斯顿/莱可扩展数据分析与人工智能中心）
In this paper the problem of image restoration (denoising and inpainting) is approached using sparse approximation of local image blocks. The local image blocks are extracted by sliding square windows over the image. An adaptive block size selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image. Ideally the adaptive local block selection yields the minimum mean square error (MMSE) in recovered image. This framework gives us a clustered image based on the selected block size, then each cluster is restored separately using sparse appr…
ClusIR: Towards Cluster-Guided All-in-One Image Restoration
Zhongnan University of Economics and Law（中南财经政法大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Yunnan University（云南大学）；Nanyang Technological University（南洋理工大学）；Wuhan University（武汉大学）
Adaptive Image Restoration for Video Surveillance: A Real-Time Approach
Datamatics Technologies（Datamatics 技术公司）；SaH Analytics International（SaH 分析国际公司）；International Data Science Institute, INPHB（INPHB 国际数据科学研究所）；Karachi Institute of Economics and Technology（卡拉奇经济与技术学院）；Kohsar University Murree (KUM)（穆里科萨尔大学）
TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration
School of Biological Science and Medical Engineering（生物科学与医学工程学院）；State Key Laboratory of Software Development Environment（软件开发环境国家重点实验室）；Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education（教育部生物力学与机力学重点实验室）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程先进创新中心）；Beihang University（北京航空航天大学）；Sinovision Technologies (Beijing) Co., Ltd.（北京视界科技有限公司）；ByteDance Inc.（字节跳动公司）
National University of Singapore（新加坡国立大学）；Yale-NUS College（耶鲁-新加坡国立大学学院）
Most deraining works focus on rain streaks removal but they cannot deal adequately with heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or rain veiling effect significantly washes out the image, further scenes are relatively more blurry, etc. In this paper, we propose a novel method to address these problems. We put forth a 2-stage network: a physics-based backbone followed by a depth-guided GAN refinement. The first stage estimates the rain streaks, the transmission, and the atmospheric light governed by the underlying physics. To tease out these compon…
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）
Non-local self-similarity in natural images has been verified to be an effective prior for image restoration. However, most existing deep non-local methods assign a fixed number of neighbors for each query item, neglecting the dynamics of non-local correlations. Moreover, the non-local correlations are usually based on pixels, prone to be biased due to image degradation. To rectify these weaknesses, in this paper, we propose a dynamic attentive graph learning model (DAGL) to explore the dynamic non-local property on patch level for image restoration. Specifically, we propose an improved graph …
Northwestern Polytechnical University（西北工业大学）；The University of Adelaide（阿德莱德大学）
In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recovering sharp and clean results from the captured images heavily depends on the ability of restoration methods and the quality of the input. Although existing research on image restoration focuses on developing models for obtaining better restored results, fewer have studied to evaluate how and which input image leads to superior restored quality. In this paper, to better study an image's potential value that can be explo…
Michigan State University（密歇根州立大学）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
We present a general learning-based solution for restoring images suffering from spatially-varying degradations. Prior approaches are typically degradation-specific and employ the same processing across different images and different pixels within. However, we hypothesize that such spatially rigid processing is suboptimal for simultaneously restoring the degraded pixels as well as reconstructing the clean regions of the image. To overcome this limitation, we propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts computation to difficult regio…
School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；Hisense Group（海信集团）
This paper presents a super-resolution method based on gradient-based adaptive interpolation. In this method, in addition to considering the distance between the interpolated pixel and the neighboring valid pixel, the interpolation coefficients take the local gradient of the original image into account. The smaller the local gradient of a pixel is, the more influence it should have on the interpolated pixel. And the interpolated high resolution image is finally deblurred by the application of wiener filter. Experimental results show that our proposed method not only substantially improves the …
Nanjing University of Science and Technology（南京理工大学）；Michigan State University（密歇根州立大学）
Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the long-term dependency problem is rarely realized for these very deep models, which results in the prior states/layers having little influence on the subsequent ones. Motivated by the fact that human thoughts have persistency, we propose a very deep persistent memory network (MemNet) that introduces a memory block, consisting of a recursive unit and a gate unit, to explicitly mine persistent memory through an adaptive learning process. The re…
University of Science and Technology Beijing（北京科技大学）；Graz University of Technology（格拉茨技术大学）
Speckle reduction is a prerequisite for many image processing tasks in synthetic aperture radar (SAR) images, as well as all coherent images. In recent years, predominant state-of-the-art approaches for despeckling are usually based on nonlocal methods which mainly concentrate on achieving utmost image restoration quality, with relatively low computational efficiency. Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality. To this end, we exploit a newly-developed trainable nonlinear reaction diffusion(TNRD) f…
Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet and alike. They are the basic tool in image compression, in image restoration, in image re-sampling and geometrical transformations and can be traced back to early 1970-ths. The paper presents a review of these methods with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive transform domain filters for image restoration, to methods of precise image re-sampling and …
CMAP, CNRS, École Polytechnique, Institut Polytechnique de Paris（巴黎综合理工学院综合理工学院应用数学中心、法国国家科学研究中心）；University of Bologna（博洛尼亚大学）
In this paper we present a new regularization term for variational image restoration which can be regarded as a space-variant anisotropic extension of the classical isotropic Total Variation (TV) regularizer. The proposed regularizer comes from the statistical assumption that the gradients of the target image distribute locally according to space-variant bivariate Laplacian distributions. The highly flexible variational structure of the corresponding regularizer encodes several free parameters which hold the potential for faithfully modelling the local geometry in the image and describing loca…
