A review of Bayesian restoration of digital images based on Monte Carlo techniques is presented. The topics covered include Likelihood, Prior and Posterior distributions, Poisson, Binay symmetric channel, and Gaussian channel models of Likelihood distribution,Ising and Potts spin models of Prior distribution, restoration of an image through Posterior maximization, statistical estimation of a true image from Posterior ensembles, Markov Chain Monte Carlo methods and cluster algorithms.
Photon-limited images are often seen in fields such as medical imaging. Although the number of collected photons on an image sensor statistically follows Poisson distribution, this type of noise is intractable, unlike Gaussian noise. In this study, we propose a Bayesian restoration method of Poisson corrupted image using Integrated Nested Laplace Approximation (INLA), which is a computational method to evaluate marginalized posterior distributions of latent Gaussian models (LGMs). When the original image can be regarded as ICAR (intrinsic conditional auto-regressive) model reasonably, our meth…
This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF). Unlike related models based on Markov Random Fields (MRF), our approach explicitly formulates the posterior distribution for the entire image. The potential functions are taken as proportional to the product of a likelihood and prior for each patch. By assuming identical parameters for similar patches, our approach can be classified as a model-based non-local method. For the prior term in the potential function of the CRF model, multivariate Gaussians and multivariate sc…
Data61-Commonwealth Scientific Industrial Research Organization (CSIRO)（Data61-联邦科学与工业研究组织）；Australian National University（澳大利亚国立大学）
Deep convolutional neural networks perform better on images containing spatially invariant degradations, also known as synthetic degradations; however, their performance is limited on real-degraded photographs and requires multiple-stage network modeling. To advance the practicability of restoration algorithms, this paper proposes a novel single-stage blind real image restoration network (R$^2$Net) by employing a modular architecture. We use a residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthe…
TDiR: Transformer based Diffusion for Image Restoration Tasks
Department of Computer Vision（计算机视觉系）；FutureDataMinds；Electrical Engineering Dept（电气工程系）；King Fahd University of Petroleum and Minerals（国王法赫德石油和矿产大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Western Australia（西澳大学）；School of Computing（计算学院）
We treat an image restoration problem with a Poisson noise chan- nel using a Bayesian framework. The Poisson randomness might be appeared in observation of low contrast object in the field of imaging. The noise observation is often hard to treat in a theo- retical analysis. In our formulation, we interpret the observation through the Poisson noise channel as a likelihood, and evaluate the bound of it with a Gaussian function using a latent variable method. We then introduce a Gaussian Markov random field (GMRF) as the prior for the Bayesian approach, and derive the posterior as a Gaussian dist…
PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation
Institute of Data Science, National Cheng Kung University（成功大学数据科学研究所）；Graduate Institute of Communication Engineering, National Taiwan University（台湾大学通讯工程学系）；NVIDIA（英伟达公司）
Heriot-Watt University（赫瑞-瓦特大学）；University of Edinburgh（爱丁堡大学）
Optical endomicroscopy (OEM) is an emerging technology platform with preclinical and clinical imaging applications. Pulmonary OEM via fibre bundles has the potential to provide in vivo, in situ molecular signatures of disease such as infection and inflammation. However, enhancing the quality of data acquired by this technique for better visualization and subsequent analysis remains a challenging problem. Cross coupling between fiber cores and sparse sampling by imaging fiber bundles are the main reasons for image degradation, and poor detection performance (i.e., inflammation, bacteria, etc.).…
Generative Iris Prior Embedded Transformer for Iris Restoration
School of Integrated Circuits, Beijing University of Posts and Telecommunications（北京邮电大学集成电路学院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
针对复杂退化虹膜图像恢复难题，提出嵌入生成式虹膜先验的 Transformer 模型（Gformer），通过分层编解码网络结合 GAN 先验与长距离依赖建模，显著提升了虹膜恢复与识别性能。
Harbin Institute of Technology at Shenzhen（哈尔滨工业大学（深圳））；Tencent（腾讯）；The Chinese University of Hong Kong at Shenzhen（香港中文大学（深圳））
Restoring the clean background from the superimposed images containing a noisy layer is the common crux of a classical category of tasks on image restoration such as image reflection removal, image deraining and image dehazing. These tasks are typically formulated and tackled individually due to the diverse and complicated appearance patterns of noise layers within the image. In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise. Our proposed DMGN…
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In this paper, a novel deep learning-based method for this task is proposed, by learning a non-linear end-to-end mapping between the noisy and clean HSIs with a combined spatial-spectral deep convolutional neural network (HSID-CNN). Both the spatial and spectral information are simultaneously assigned to the proposed network. In addition, multi-scale feature extraction and multi-level feature representation are respectively employed to captu…
Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Hong Kong University of Science and Technology（香港科技大学）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Samsung Research China - Beijing (SRC-B)（三星中国研究院北京分部）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳校区）；SAMSUNG Electronics（三星电子）
It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision. Recent years have seen the emergence of two main approaches for learning the parameters in MRFs: (1) probabilistic learning using sampling-based algorithms and (2) loss-specific training based on MAP estimate. After investigating existing training approaches, it turns out that the performance of the loss-specific training has been significantly underestimated in existing work. In this paper, we revisit this approach and use techniques from bi-level optimization to sol…
A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing
School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）；Hunan Key Laboratory for Computation and Simulation in Science and Engineering（湖南省计算与模拟科学工程重点实验室）；Key Laboratory for Intelligent Computing and Information Processing of the Ministry of Education（教育部智能计算与信息处理重点实验室）；National Center for Applied Mathematics in Hunan（湖南省应用数学中心）；Hunan International Scientific and Technological Innovation Cooperation Base of Computational Science（湖南省 computational science 国际科学和技术创新合作基地）；College of Mathematical Medicine, Zhejiang Normal University（浙江师范大学数学医学院）；School of Automation and Electronic Information, Xiangtan University（湘潭大学自动化与电子信息学院）
Color image restoration based on nonlocal saturation-value similarity
School of Mathematical Sciences, Key Laboratory of Intelligent Computing and Applications (Ministry of Education), Tongji University（数学科学学院、智能计算与应用重点实验室（教育部）、同济大学）
3D Priors-Guided Diffusion for Blind Face Restoration
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Yunnan Key Laboratory of Smart Education（云南省智慧教育重点实验室）；Tencent Youtu Lab（腾讯优图实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent Cloud Architecture Platform（腾讯云架构平台）
National Chung Hsing University（国立中兴大学）；National Taichung University of Science and Technology（国立台中科技大学）
Image restoration is a low-level vision task which is to restore degraded images to noise-free images. With the success of deep neural networks, the convolutional neural networks surpass the traditional restoration methods and become the mainstream in the computer vision area. To advance the performanceof denoising algorithms, we propose a blind real image denoising network (SRMNet) by employing a hierarchical architecture improved from U-Net. Specifically, we use a selective kernel with residual block on the hierarchical structure called M-Net to enrich the multi-scale semantic information. F…
Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis
University of Macau（澳门大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Big Data, Baoshan University（保山学院大数据学院）；School of Computer Science and Engineering, Huizhou University（惠州学院计算机科学与工程学院）
提出 Color Balance Prior 引导的多尺度混合感知框架 GuidedHybSensUIR，并构建综合水下复原基准，实验显示其总体优于37种现有方法。
