Classification for degraded images having various levels of degradation is very important in practical applications. This paper proposes a convolutional neural network to classify degraded images by using a restoration network and an ensemble learning. The results demonstrate that the proposed network can classify degraded images over various levels of degradation well. This paper also reveals how the image-quality of training data for a classification network affects the classification performance of degraded images.
Shanghai Jiao Tong University（上海交通大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；RIKEN AIP（理化学研究所革新智能综合研究中心）；The University of Tokyo（东京大学）；Innopeak Technology；The University of British Columbia（不列颠哥伦比亚大学）；Shanghai AI Laboratory（上海人工智能实验室）
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in degraded images. However, most of these algorithms assume the degradation is fixed and known a priori. When the real degradation is unknown or differs from assumption, both the pre-processing module and the consequent high-level task such as object detection would fail. Here, we propose a novel framework, RestoreDet, to detect objects in degraded low resolution images. RestoreDet utilizes the downsampling degradation as a kind of transformation for self-supervised signals…
Degraded Infrared Small Object Detection via Degradation-Adapted Physics-Guided Restoration
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Ocean Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院海洋工程学院）
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 …
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）
MvKSR: Multi-view Knowledge-guided Scene Recovery for Hazy and Rainy Degradation
The Hong Kong Polytechnic University（香港理工大学）；Wuhan University of Technology（武汉理工大学）；Wuhan Baosight Software Co., Ltd（武汉宝信软件有限公司）；Singapore Management University（新加坡管理大学）
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Learning-based methods especially with convolutional neural networks (CNN) are continuously showing superior performance in computer vision applications, ranging from image classification to restoration. For image classification, most existing works focus on very clean images such as images in Caltech-256 and ImageNet datasets. However, in most realistic scenarios, the acquired images may suffer from degradation. One important and interesting problem is to combine image classification and restoration tasks to improve the performance of CNN-based classification networks on degraded images. In t…
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）
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…
Shanghai Jiao Tong University（上海交通大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Shanghai AI Laboratory（上海人工智能实验室）
Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. This gap between the assumed and actual degradation hurts the restoration performance where artifacts are often observed in the output. However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. To tackle this robustness issue, we propose Diffusion-based Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-inv…
Indian Institute of Technology, Madras（马德拉斯印度理工学院）
Real low-resolution (LR) face images contain degradations which are too varied and complex to be captured by known downsampling kernels and signal-independent noises. So, in order to successfully super-resolve real faces, a method needs to be robust to a wide range of noise, blur, compression artifacts etc. Some of the recent works attempt to model these degradations from a dataset of real images using a Generative Adversarial Network (GAN). They generate synthetically degraded LR images and use them with corresponding real high-resolution(HR) image to train a super-resolution (SR) network usi…
Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction
School of Astronautics Beihang University Beijing China（航天学院 北航 北京 中国）；School of Artificial Intelligence Beihang University Beijing China（人工智能学院 北航 北京 中国）；Zhongguancun Academy Beijing China（中关村学院 北京 中国）；Beihang University School of Astronautics Beijing China（北航 航天学院 北京 中国）；Beihang University（北航）；Zhongguancun Academy（中关村学院）
Institute of Cyber-System and Control, Zhejiang University（浙江大学智能系统与控制研究所）；Ningbo Research Institute, Zhejiang University（浙江大学宁波研究院）；State Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）
In spite of the success on benchmark datasets, most advanced face super-resolution models perform poorly in real scenarios since the remarkable domain gap between the real images and the synthesized training pairs. To tackle this problem, we propose a novel domain-adaptive degradation network for face super-resolution in the wild. This degradation network predicts a flow field along with an intermediate low resolution image. Then, the degraded counterpart is generated by warping the intermediate image. With the preference of capturing motion blur, such a model performs better at preserving ide…
Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；AI Center–Toronto, Samsung Electronics（三星电子多伦多AI中心）；University Health Network（大学健康网络）；York University（约克大学）
Latent Diffusion Based Face Enhancement under Degraded Conditions for Forensic Face Recognition
Centre for Visual Computing and Intelligent Systems（视觉计算与智能系统中心）；Unviversity of Bradford（布拉德福德大学）；General Department of Forensic Science and Criminology（法医学与犯罪学部门）；Dubai Police Headquarters（迪拜警察总部）；theCircle Ltd（theCircle公司）
Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong Polytechnic University（香港理工大学）；Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；Peng Cheng Lab（鹏城实验室）
How to design proper training pairs is critical for super-resolving real-world low-quality (LQ) images, which suffers from the difficulties in either acquiring paired ground-truth high-quality (HQ) images or synthesizing photo-realistic degraded LQ observations. Recent works mainly focus on modeling the degradation with handcrafted or estimated degradation parameters, which are however incapable to model complicated real-world degradation types, resulting in limited quality improvement. Notably, LQ face images, which may have the same degradation process as natural images, can be robustly rest…
DACESR: Degradation-Aware Conditional Embedding for Real-World Image Super-Resolution
School of Electrical and Information Engineering, Zhengzhou University of Light Industry（郑州轻工业大学电气与信息工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Automation, Central South University（中南大学自动化学院）；Intelligent Manufacturing Engineering at Machinery Technology Development Co.,Ltd.（机械技术发展有限公司智能制造工程）；China Academy of Machinery Science and Technology Group Co.,Ltd.（中国机械科学与技术集团有限公司）
Removing adverse weather conditions like rain, fog, and snow from images is an important problem in many applications. Most methods proposed in the literature have been designed to deal with just removing one type of degradation. Recently, a CNN-based method using neural architecture search (All-in-One) was proposed to remove all the weather conditions at once. However, it has a large number of parameters as it uses multiple encoders to cater to each weather removal task and still has scope for improvement in its performance. In this work, we focus on developing an efficient solution for the a…
High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification
Institute of Remote Sensing and Geographic Information System, Peking University（遥感与地理信息系统研究所，北京大学）；Beijing Key Laboratory of Spatio-temporal Perception and Urban Resilience, Peking University（北京时空感知与城市韧性重点实验室，北京大学）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；Institute of energy, Peking University（北京大学能源研究所）；School of Mechanics and Engineering Science, Peking University（北京大学力学与工程科学学院）；School of Automation, Central South University（中南大学自动化学院）
Learning Heavily-Degraded Prior for Underwater Object Detection
DUT-RU International School of Information Science, Dalian University of Technology（大连理工大学-俄罗斯国立高等经济大学信息科学国际学院）；Dalian University of Technology（大连理工大学）
Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index of refraction of the atmosphere. Variations in the refractive index causes the captured images to be geometrically distorted and blurry. Hence, it is important to compensate for the visual degradation in images caused by atmospheric turbulence. In this paper, we propose a deep learning-based approach for restring a single image degraded by atmospheric turbulence. We make use of the epistemic uncertainty based on Monte…
Degradation-Consistent Paired Training for Robust AI-Generated Image Detection
Department of Computer Science（计算机科学系）；University College London（伦敦大学学院）；Department of Earth Science and Engineering（地球科学与工程系）；Imperial College London（伦敦帝国理工学院）；School of Electronic Information（电子信息学院）
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…
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 …
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）
Typical methods for blind image super-resolution (SR) focus on dealing with unknown degradations by directly estimating them or learning the degradation representations in a latent space. A potential limitation of these methods is that they assume the unknown degradations can be simulated by the integration of various handcrafted degradations (e.g., bicubic downsampling), which is not necessarily true. The real-world degradations can be beyond the simulation scope by the handcrafted degradations, which are referred to as novel degradations. In this work, we propose to learn a latent representa…
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）
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…
Although many long-range imaging systems are designed to support extended vision applications, a natural obstacle to their operation is degradation due to atmospheric turbulence. Atmospheric turbulence causes significant degradation to image quality by introducing blur and geometric distortion. In recent years, various deep learning-based single image atmospheric turbulence mitigation methods, including CNN-based and GAN inversion-based, have been proposed in the literature which attempt to remove the distortion in the image. However, some of these methods are difficult to train and often fail…
Shanghai Jiao Tong University（上海交通大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；RIKEN AIP（理化学研究所先进智能项目中心）；The University of Tokyo（东京大学）；The University of British Columbia（不列颠哥伦比亚大学）；Shanghai AI Laboratory（上海人工智能实验室）
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images. Most of these algorithms assume the degradation is fixed and known a priori. However, in practical, either the real degradation or optimal up-sampling ratio rate is unknown or differs from assumption, leading to a deteriorating performance for both the pre-processing module and the consequent high-level task such as object detection. Here, we propose a novel self-supervised framework to detect objects in degraded low resolution images. We utilizes the …
DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging
School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable. On the other hand, intermediate references of degraded quality are often available, e.…
