State-of-the-art algorithms for imaging inverse problems (namely deblurring and reconstruction) are typically iterative, involving a denoising operation as one of its steps. Using a state-of-the-art denoising method in this context is not trivial, and is the focus of current work. Recently, we have proposed to use a class-adapted denoiser (patch-based using Gaussian mixture models) in a so-called plug-and-play scheme, wherein a state-of-the-art denoiser is plugged into an iterative algorithm, leading to results that outperform the best general-purpose algorithms, when applied to an image of a known class (e.g. faces, text, brain MRI). In this paper, we extend that approach to handle situations where the image being processed is from one of a collection of possible classes or, more importantly, contains regions of different classes. More specifically, we propose a method to locally select one of a set of class-adapted Gaussian mixture patch priors, previously estimated from clean images of those classes. Our approach may be seen as simultaneously performing segmentation and restoration, thus contributing to bridging the gap between image restoration/reconstruction and analysis.
This paper proposes using a Gaussian mixture model as a prior, for solving two image inverse problems, namely image deblurring and compressive imaging. We capitalize on the fact that variable splitting algorithms, like ADMM, are able to decouple the handling of the observation operator from that of the regularizer, and plug a state-of-the-art algorithm into the pure denoising step. Furthermore, we show that, when applied to a specific type of image, a Gaussian mixture model trained from an database of images of the same type is able to outperform current state-of-the-art methods.
In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for raw image restoration. ELMformer contains two core designs especially for raw images whose primitive attribute is single-channel. The first design is a Bi-directional Fusion Projection (BFP) module, where we consider both the color characteristics of raw images and spatial structure of single-channel. The second one is that we propose a Locally Multiplicative Self-Attention (L-MSA) scheme to effectively deliver infor…
Also recently, exciting strides forward have been made in the area of image restoration, particularly for image denoising and single image super-resolution. Deep learning techniques contributed to this significantly. The top methods differ in their formulations and assumptions, so even if their average performance may be similar, some work better on certain image types and image regions than others. This complementarity motivated us to propose a novel 3D convolutional fusion (3DCF) method. Unlike other methods adapted to different tasks, our method uses the exact same convolutional network arc…
Plug-and-Play optimization recently emerged as a powerful technique for solving inverse problems by plugging a denoiser into a classical optimization algorithm. The denoiser accounts for the regularization and therefore implicitly determines the prior knowledge on the data, hence replacing typical handcrafted priors. In this paper, we extend the concept of plug-and-play optimization to use denoisers that can be parameterized for non-constant noise variance. In that aim, we introduce a preconditioning of the ADMM algorithm, which mathematically justifies the use of such an adjustable denoiser. …
Universität Innsbruck（因斯布鲁克大学）；Universität Graz（格拉茨大学）；Research Center for Non Destructive Testing GmbH（非破坏性测试研究中心 GmbH）；Prospective Instruments LK OG（前瞻性仪器 LK OG）
University of Science and Technology of China（中国科学技术大学）；University of Macau（澳门大学）；University of Chinese Academy of Sciences（中国科学院大学）
In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using the Transformer block. In Uformer, there are two core designs. First, we introduce a novel locally-enhanced window (LeWin) Transformer block, which performs nonoverlapping window-based self-attention instead of global self-attention. It significantly reduces the computational complexity on high resolution feature map while capturing local context. Second, we propose a learnable multi-scale restoration modulator in the…
Blind image deblurring (BID) is an ill-posed inverse problem, usually addressed by imposing prior knowledge on the (unknown) image and on the blurring filter. Most of the work on BID has focused on natural images, using image priors based on statistical properties of generic natural images. However, in many applications, it is known that the image being recovered belongs to some specific class (e.g., text, face, fingerprints), and exploiting this knowledge allows obtaining more accurate priors. In this work, we propose a method where a Gaussian mixture model (GMM) is used to learn a class-adap…
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）
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）
Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration
School of Aeronautics and Astronautics, Shanghai Jiao Tong University（上海交通大学航空航天学院）；Land Satellite Remote Sensing Application Center（陆地卫星遥感应用中心）；Ministry of Natural Resources of the People’s Republic of China（中华人民共和国自然资源部）；China-ASEAN Satellite Remote Sensing Applications（中国-东盟卫星遥感应用（中心））
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…
Laboratoire d’Informatique de l’École Normale Supérieure（巴黎高等师范学院计算机实验室）；INRIA（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；University of California, Berkeley（加州大学伯克利分校）
This paper proposes a novel approach to image deblurring and digital zooming using sparse local models of image appearance. These models, where small image patches are represented as linear combinations of a few elements drawn from some large set (dictionary) of candidates, have proven well adapted to several image restoration tasks. A key to their success has been to learn dictionaries adapted to the reconstruction of small image patches. In contrast, recent works have proposed instead to learn dictionaries which are not only adapted to data reconstruction, but also tuned for a specific task.…
City University of Hong Kong（香港城市大学）；Noah’s Ark Lab, Huawei Technologies（华为技术有限公司诺亚方舟实验室）
The latest advances in full-reference image quality assessment (IQA) involve unifying structure and texture similarity based on deep representations. The resulting Deep Image Structure and Texture Similarity (DISTS) metric, however, makes rather global quality measurements, ignoring the fact that natural photographic images are locally structured and textured across space and scale. In this paper, we describe a locally adaptive structure and texture similarity index for full-reference IQA, which we term A-DISTS. Specifically, we rely on a single statistical feature, namely the dispersion index…
In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in paral…
Zhejiang University（浙江大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；ETH Zürich（苏黎世联邦理工学院）
Panoramic Annular Lens (PAL) composed of few lenses has great potential in panoramic surrounding sensing tasks for mobile and wearable devices because of its tiny size and large Field of View (FoV). However, the image quality of tiny-volume PAL confines to optical limit due to the lack of lenses for aberration correction. In this paper, we propose an Annular Computational Imaging (ACI) framework to break the optical limit of light-weight PAL design. To facilitate learning-based image restoration, we introduce a wave-based simulation pipeline for panoramic imaging and tackle the synthetic-to-re…
Duke University（杜克大学）；Télécom ParisTech, Université Paris-Saclay（巴黎高科电信学院，巴黎-萨克雷大学）；Université Paris Descartes（巴黎笛卡尔大学）；Universidad de la República（共和国大学）
Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems…
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程研究中心，计算机学院，武汉大学）；Aerospace Information Research Institute, Henan Academy of Sciences（航天信息研究所，河南科学院）
Pansharpening techniques aim at fusing low-resolution multispectral (MS) images and high-resolution panchromatic (PAN) images to produce high-resolution MS images. Despite significant progress in the field, spectral and spatial distortions might still compromise the quality of the results. We introduce a restoration strategy to mitigate artifacts of fused products. After applying the Principal Component Analysis (PCA) transform to a pansharpened image, the chromatic components are filtered conditionally to the geometry of PAN. The structural component is then replaced by the locally histogram-…
Recovery of true color from underwater images is an ill-posed problem. This is because the wide-band attenuation coefficients for the RGB color channels depend on object range, reflectance, etc. which are difficult to model. Also, there is backscattering due to suspended particles in water. Thus, most existing deep-learning based color restoration methods, which are trained on synthetic underwater datasets, do not perform well on real underwater data. This can be attributed to the fact that synthetic data cannot accurately represent real conditions. To address this issue, we use an image to im…
Southern University of Science and Technology（南方科技大学）；Ningbo Institute of Industrial Technology, Chinese Academy of Sciences（中国科学院宁波工业技术研究院）；Cixi Institute of Biomedical Engineering（慈溪生物医学工程研究所）；Agency for Science, Technology and Research（新加坡科技研究局）；Institute of High Performance Computing（高性能计算研究所）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）
Cataracts are the leading cause of vision loss worldwide. Restoration algorithms are developed to improve the readability of cataract fundus images in order to increase the certainty in diagnosis and treatment for cataract patients. Unfortunately, the requirement of annotation limits the application of these algorithms in clinics. This paper proposes a network to annotation-freely restore cataractous fundus images (ArcNet) so as to boost the clinical practicability of restoration. Annotations are unnecessary in ArcNet, where the high-frequency component is extracted from fundus images to repla…
A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与测绘工程学院）；Nanjing Center, China Geological Survey（中国地质调查局南京中心）；School of Automation, Southeast University（东南大学自动化学院）
NYU Tandon School of Engineering（纽约大学坦登工程学院）；Riverside Research（河畔研究公司）；University of Hawaii（夏威夷大学）；Kuakini Medical Center（库阿基尼医疗中心）
Previous studies by our group have shown that three-dimensional high-frequency quantitative ultrasound methods have the potential to differentiate metastatic lymph nodes from cancer-free lymph nodes dissected from human cancer patients. To successfully perform these methods inside the lymph node parenchyma, an automatic segmentation method is highly desired to exclude the surrounding thin layer of fat from quantitative ultrasound processing and accurately correct for ultrasound attenuation. In high-frequency ultrasound images of lymph nodes, the intensity distribution of lymph node parenchyma …
