Infrared imaging and turbulence strength measurements are in widespread demand in many fields. This paper introduces a Physical Prior Guided Cooperative Learning (P2GCL) framework to jointly enhance atmospheric turbulence strength estimation and infrared image restoration. P2GCL involves a cyclic collaboration between two models, i.e., a TMNet measures turbulence strength and outputs the refractive index structure constant (Cn2) as a physical prior, a TRNet conducts infrared image sequence restoration based on Cn2 and feeds the restored images back to the TMNet to boost the measurement accuracy. A novel Cn2-guided frequency loss function and a physical constraint loss are introduced to align the training process with physical theories. Experiments demonstrate P2GCL achieves the best performance for both turbulence strength estimation (improving Cn2 MAE by 0.0156, enhancing R2 by 0.1065) and image restoration (enhancing PSNR by 0.2775 dB), validating the significant impact of physical prior guided cooperative learning.
Blind super-resolution (SR) aims to recover high-quality visual textures from a low-resolution (LR) image, which is usually degraded by down-sampling blur kernels and additive noises. This task is extremely difficult due to the challenges of complicated image degradations in the real-world. Existing SR approaches either assume a predefined blur kernel or a fixed noise, which limits these approaches in challenging cases. In this paper, we propose a Degradation-guided Meta-restoration network for blind Super-Resolution (DMSR) that facilitates image restoration for real cases. DMSR consists of a …
Tsinghua University（清华大学）；Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science（中国科学院深圳先进技术研究院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；University College London（伦敦大学学院）
Most state-of-the-art semantic segmentation approaches only achieve high accuracy in good conditions. In practically-common but less-discussed adverse environmental conditions, their performance can decrease enormously. Existing studies usually cast the handling of segmentation in adverse conditions as a separate post-processing step after signal restoration, making the segmentation performance largely depend on the quality of restoration. In this paper, we propose a novel deep-learning framework to tackle semantic segmentation and image restoration in adverse environmental conditions in a hol…
Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark
Key Laboratory of Intelligent Computing & Signal Processing (Anhui University), Ministry of Education（教育部智能计算与信号处理重点实验室（安徽大学））；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
Tokyo Institute of Technology（东京工业大学）；Kadinche Corporation（Kadinche公司）；National Institute of Advanced Industrial Science and Technology（国立先进工业科学技术研究所）
Image restoration from a single image degradation type, such as blurring, hazing, random noise, and compression has been investigated for decades. However, image degradations in practice are often a mixture of several types of degradation. Such compositional degradations complicate restoration because they require the differentiation of different degradation types and levels. In this paper, we propose a convolutional neural network (CNN) model for estimating the degradation properties of a given degraded image. Furthermore, we introduce an image restoration CNN model that adopts the estimated …
Image restoration is the task of recovering a clean image from a degraded version. In most cases, the degradation is spatially varying, and it requires the restoration network to both localize and restore the affected regions. In this paper, we present a new approach suitable for handling the image-specific and spatially-varying nature of degradation in images affected by practically occurring artifacts such as blur, rain-streaks. We decompose the restoration task into two stages of degradation localization and degraded region-guided restoration, unlike existing methods which directly learn a …
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning
Beihang University（北京航空航天大学）；Image Processing Center（图像处理中心）；The State Key Laboratory of Virtual Reality Technology and Systems（虚拟现实技术与系统国家重点实验室）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程高精尖创新中心）
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（多模态人工智能系统国家重点实验室（MAIS），中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Ant Group（蚂蚁集团）；PeopleAI Inc.（PeopleAI公司）
Perceptual image restoration seeks for high-fidelity images that most likely degrade to given images. For better visual quality, previous work proposed to search for solutions within the natural image manifold, by exploiting the latent space of a generative model. However, the quality of generated images are only guaranteed when latent embedding lies close to the prior distribution. In this work, we propose to restrict the feasible region within the prior manifold. This is accomplished with a non-parametric metric for two distributions: the Maximum Mean Discrepancy (MMD). Moreover, we model th…
Degradation-Aware Metric Prompting for Hyperspectral Image Restoration
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China（北京理工大学计算机科学与技术学院）；School of Computer Science, Wuhan University, Wuhan, Hubei, China（武汉大学计算机学院）；Zhongguancun Academy, Beijing, China（中关村学院）
Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition
Xi’an University of Architecture and Technology（西安建筑科技大学）；Northwestern Polytechnical University（西北工业大学）；University of Macau（澳门大学）；Xi’an Institute of Optics and Precision Mechanics of CAS（中国科学院西安光学精密机械研究所）
The deep-learning-based image restoration and fusion methods have achieved remarkable results. However, the existing restoration and fusion methods paid little research attention to the robustness problem caused by dynamic degradation. In this paper, we propose a novel dynamic image restoration and fusion neural network, termed as DDRF-Net, which is capable of solving two problems, i.e., static restoration and fusion, dynamic degradation. In order to solve the static fusion problem of existing methods, dynamic convolution is introduced to learn dynamic restoration and fusion weights. In additi…
Optimal Transport Based Unsupervised Restoration Learning Exploiting Degradation Sparsity
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, China（信息科学与电子工程学院，上海交通大学）；School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China（通信工程学院，杭州电子科技大学）
DVANet: Degradation-aware Visual-prior Alignment Network for Image Restoration
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Shenzhen Research Institute of Northwestern Polytechnical University（西北工业大学深圳研究院）；State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学智慧城市物联网国家重点实验室）
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University of Advanced Technology（深圳理工大学）
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.
DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model
School of Information Engineering, Chang’an University（信息工程学院，长安大学）；School of Electronics and Information, Northwestern Polytechnical University（电子与信息学院，西北工业大学）
Degradation Frequency Curve: An Explicit Frequency-Quantified Representation for All-in-One Image Restoration
College of Electronic Science, National University of Defense Technology（国防科技大学电子科学学院）；College of Electronic Engineering, Beihang University（北航电子工程学院）；VCIP, School of Computer Science, Nankai University（南开大学计算机科学学院）
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD is …
Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（多模态人工智能系统国家重点实验室（MAIS）、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）；Ant Group（蚂蚁集团）；PeopleAI Inc.（PeopleAI公司）
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）
Tokyo Institute of Technology（东京工业大学）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）
Blind image restoration processors based on convolutional neural network (CNN) are intensively researched because of their high performance. However, they are too sensitive to the perturbation of the degradation model. They easily fail to restore the image whose degradation model is slightly different from the trained degradation model. In this paper, we propose a non-blind CNN-based image restoration processor, aiming to be robust against a perturbation of the degradation model compared to the blind restoration processor. Experimental comparisons demonstrate that the proposed non-blind CNN-ba…
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）
TIDE: Two-Stage Inverse Degradation Estimation with Guided Prior Disentanglement for Underwater Image Restoration
Mohamed bin Zayed University of AI（莫扎德·本·泽亚德人工智能大学）；University of Amsterdam（阿姆斯特丹大学）；University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）；Nagasaki University（长崎大学）
Southwest Jiaotong University（西南交通大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）
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）
