The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging sciences. In such cases, the degradation is typically caused by the resolution limitations of an imaging device in use and/or by the destructive influence of measurement noise. Specifically, when the noise obeys a Poisson probability law, standard approaches to the problem of image reconstruction are based on using fixed-point algorithms which follow the methodology first proposed by Richardson and Lucy. The practice of using these methods, however, shows that their convergence properties tend to deteriorate at relatively high noise levels. Accordingly, in the present paper, a novel method for de-noising and/or de-blurring of digital images corrupted by Poisson noise is introduced. The proposed method is derived under the assumption that the image of interest can be sparsely represented in the domain of a linear transform. Consequently, a shrinkage-based iterative procedure is proposed, which guarantees the solution to converge to the global maximizer of an associated maximum-a-posteriori criterion. It is shown in a series of both computer-simulated and real-life experiments that the proposed method outperforms a number of existing alternatives in terms of stability, precision, and computational efficiency.
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education（教育部医学影像智能计算重点实验室）；Department of Electrical and Computer Engineering, Stevens Institute of Technology（史蒂文斯理工学院电气与计算机工程系）
The iterative weighted shrinkage-thresholding algorithm (IWSTA) has shown superiority to the classic unweighted iterative shrinkage-thresholding algorithm (ISTA) for solving linear inverse problems, which address the attributes differently. This paper proposes a new entropy regularized IWSTA (ERIWSTA) that adds an entropy regularizer to the cost function to measure the uncertainty of the weights to stimulate attributes to participate in problem solving. Then, the weights are solved with a Lagrange multiplier method to obtain a simple iterative update. The weights can be explained as the probab…
The problem of restoration of digital images from their degraded measurements plays a central role in a multitude of practically important applications. A particularly challenging instance of this problem occurs in the case when the degradation phenomenon is modeled by an ill-conditioned operator. In such a case, the presence of noise makes it impossible to recover a valuable approximation of the image of interest without using some a priori information about its properties. Such a priori information is essential for image restoration, rendering it stable and robust to noise. Particularly, if …
Diverse user preferences over images have recently led to a great amount of interest in controlling the imagery effects for image restoration tasks. However, existing methods require separate inference through the entire network per each output, which hinders users from readily comparing multiple imagery effects due to long latency. To this end, we propose a novel framework based on a neural architecture search technique that enables efficient generation of multiple imagery effects via two stages of pruning: task-agnostic and task-specific pruning. Specifically, task-specific pruning learns to…
How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery
Hong Kong University of Science and Technology（香港科技大学）；Jiangnan University（江南大学）；The Chinese University of Hong Kong（香港中文大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Peking University（北京大学）
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images. This new acquisition mode allows for modern image restoration techniques, including multi-frame super-resolution. As the satellite moves during the acquisition of the burst, elevation changes in the scene translate into noticeable parallax. This parallax hinders the results of the restoration. To cope with this issue, we propose a novel parallax estimation method. The method is composed of a linear Plane+Parallax decomposition of the apparent motion and a multi-frame optical flow algo…
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Nanyang Technological University（南洋理工大学）
Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect. However, the large variety of user flavors motivates the possibility of continuous transition among different output effects. Unlike existing methods that require a specific design to achieve one particular transition (e.g., style transfer), we propose a simple yet universal approach to attain a smooth control of diverse imagery effects in many low-level vision tasks, including image restoration, image-to-image translation, and style transfer. Specifically, our …
National Taiwan University（台湾大学）；Nanjing University of Science and Technology（南京理工大学）；Google Research（谷歌研究院）；University of California, Merced（加州大学默塞德分校）；Yonsei University（延世大学）
Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. In this paper, we propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. In contrast to most existing methods that use deep convolutional neural netw…
Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling
School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Kunshan Innovation Institute of Xidian University（西安电子科技大学昆山创新研究院）；University of Montenegro（黑山大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Macquarie University（麦考瑞大学）
This paper introduces a new rain removal model based on the shrinkage of the sparse codes for a single image. Recently, dictionary learning and sparse coding have been widely used for image restoration problems. These methods can also be applied to the rain removal by learning two types of rain and non-rain dictionaries and forcing the sparse codes of the rain dictionary to be zero vectors. However, this approach can generate unwanted edge artifacts and detail loss in the non-rain regions. Based on this observation, a new approach for shrinking the sparse codes is presented in this paper. To e…
A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog
Center for Aerospace Research (CfAR), University of Victoria（维多利亚大学航空航天研究中心）；Advanced Control and Intelligent Systems (ACIS) Lab, University of Victoria（维多利亚大学先进控制与智能系统实验室）
AI-driven Dispensing of Coral Reseeding Devices for Broad-scale Restoration of the Great Barrier Reef
Queensland University of Technology（昆士兰理工大学）；QUT Centre for Robotics（昆士兰理工大学机器人中心）；School of Electrical Engineering and Robotics（电气工程与机器人学院）；Australian Institute of Marine Science（澳大利亚海洋科学研究所）
Building Extraction from Remote Sensing Imagery under Hazy and Low-light Conditions: Benchmark and Baseline
MOE Key Lab of ICSP（教育部ICSP重点实验室）；Anhui Provincial Key Lab of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；IMIS Lab of Anhui Province（安徽省IMIS实验室）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
Autonomous underwater vehicles (AUVs) rely on a variety of sensors - acoustic, inertial and visual - for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality, particularly at shallower depths. However, factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. AUVs that rely on visual sensing thus face difficult challenges, and consequently exhibit poor performance on vision-driven tasks…
LUCID: Learning Unified Control for Image Deflaring and Exposure Mastery in Nighttime Photography
MoE Key Lab of Artificial Intelligence（人工智能混合专家实验室）；AI Institute（人工智能研究所）；School of Computer Science（计算机科学学院）；School of Biomedical Engineering（生物医学工程学院）；School of Artificial Intelligence（人工智能学院）
A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing
School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）；School of Automation and Electronic Information, Xiangtan University（湘潭大学自动化与电子信息学院）
Underwater Image Restoration via Polymorphic Large Kernel CNNs
University of Macau（澳门大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Polytechnic University（深圳职业技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Task-Guided Prompting for Unified Remote Sensing Image Restoration
School of Electronic and Information Engineering, Ningbo University of Technology（宁波工程学院电子与信息工程学院）；University of Exeter（埃克塞特大学）；Engineering Research Center of Intelligent Finance, Ministry of Education（教育部智能金融工程研究中心）；School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）
Institut de Mathématiques de Bordeaux, CNRS, Université de Bordeaux, Bordeaux INP（波尔多数学研究所、法国国家科学研究中心、波尔多大学、波尔多国立理工学院）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）
Patch priors have become an important component of image restoration. A powerful approach in this category of restoration algorithms is the popular Expected Patch Log-Likelihood (EPLL) algorithm. EPLL uses a Gaussian mixture model (GMM) prior learned on clean image patches as a way to regularize degraded patches. In this paper, we show that a generalized Gaussian mixture model (GGMM) captures the underlying distribution of patches better than a GMM. Even though GGMM is a powerful prior to combine with EPLL, the non-Gaussianity of its components presents major challenges to be applied to a comp…
Pengcheng Laboratory（鹏城实验室）；Dalian University of Technology（大连理工大学）
Deep learning for image super-resolution (SR) has been investigated by numerous researchers in recent years. Most of the works concentrate on effective block designs and improve the network representation but lack interpretation. There are also iterative optimization-inspired networks for image SR, which take the solution step as a whole without giving an explicit optimization step. This paper proposes an unfolding iterative shrinkage thresholding algorithm (ISTA) inspired network for interpretable image SR. Specifically, we analyze the problem of image SR and propose a solution based on the I…
University of Notre Dame（圣母大学）；Honeywell ACST（霍尼韦尔先进概念与科学技术（ACST））；Northwestern University（西北大学）；Zendar Co.（Zendar公司）；Texas A&M University（德克萨斯农工大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）；National Tsing Hua University（国立清华大学）；Johns Hopkins University（约翰霍普金斯大学）；Noblis（诺布利斯（Noblis））
What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step to improve image interpretability for manual analysis or automatic visual recognition to classify scene content? While there have been important advances in the area of computational photography to restore or enhance the visual quality of an image, the capabilities of such techniques have not always translated in a useful way to visual recognition tasks. Consequently, there is a pressi…
Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）科学与工程学院）；Geosciences and Engineering, Southwest Jiaotong University（西南交通大学地球科学与工程学院）；Institute of Space and Earth Information Science and the Department of Geography and Resource Management, The Chinese University of Hong Kong（香港中文大学空间与地球信息科学研究所及地理与资源管理系）
