We study an ill-posed problem of geological field reconstruction under limited observations. Engineers often have to deal with the problem of reconstructing the subsurface geological field from sparse measurements such as exploration well data. Inspired by image inpainting, we model this partially observed spatial field as a multidimensional tensor and recover missing values by enforcing a global low rank structure together with spatial smoothness. We solve the resulting optimization via the Alternating Direction Method of Multipliers. On the SPE10 model 2 benchmark, this deterministic approach yields consistently lower relative squared error than ordinary kriging across various sampling densities and produces visually coherent reconstructions.
Indian Institute of Technology Gandhinagar（印度理工学院甘地讷格尔分校）
Image Inpainting is one of the very popular tasks in the field of image processing with broad applications in computer vision. In various practical applications, images are often deteriorated by noise due to the presence of corrupted, lost, or undesirable information. There have been various restoration techniques used in the past with both classical and deep learning approaches for handling such issues. Some traditional methods include image restoration by filling gap pixels using the nearby known pixels or using the moving average over the same. The aim of this paper is to perform image inpa…
School of Mathematical Sciences, Dalian University of Technology（大连理工大学数学科学学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing infor- mation in corrupted regions. However, directly using exist- ing residual learn…
AsyncDSB: Schedule-Asynchronous Diffusion Schrödinger Bridge for Image Inpainting
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；ShenZhen SiFar Co., Ltd.（深圳思远有限公司）；Centre for Frontier AI Research（前沿人工智能研究中心）；Shengshu AI（圣树人工智能）
The Hong Kong University of Science and Technology（香港科技大学）
Although recent inpainting approaches have demonstrated significant improvements with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions. To address these issues, we propose an external-internal inpainting scheme with a monochromic bottleneck that helps image inpainting models remove these artifacts. In the external learning stage, we reconstruct missing structures and details in the monochromic space to reduce the learning dimension. In the internal learning stage, we propose a novel internal color propagation …
Image inpainting aims at restoring missing region of corrupted images, which has many applications such as image restoration and object removal. However, current GAN-based inpainting models fail to explicitly consider the semantic consistency between restored images and original images. Forexample, given a male image with image region of one eye missing, current models may restore it with a female eye. This is due to the ambiguity of GAN-based inpainting models: these models can generate many possible restorations given a missing region. To address this limitation, our key insight is that sema…
Image inpainting is a technique of completing missing pixels such as occluded region restoration, distracting objects removal, and facial completion. Among these inpainting tasks, facial completion algorithm performs face inpainting according to the user direction. Existing approaches require delicate and well controlled input by the user, thus it is difficult for an average user to provide the guidance sufficiently accurate for the algorithm to generate desired results. To overcome this limitation, we propose an alternative user-guided inpainting architecture that manipulates facial attribute…
We present an end-to-end deep learning framework for indoor panoramic image inpainting. Although previous inpainting methods have shown impressive performance on natural perspective images, most fail to handle panoramic images, particularly indoor scenes, which usually contain complex structure and texture content. To achieve better inpainting quality, we propose to exploit both the global and local context of indoor panorama during the inpainting process. Specifically, we take the low-level layout edges estimated from the input panorama as a prior to guide the inpainting model for recovering …
In this paper, we propose a GAN-based approach for gap filling in borehole images created by wireline microresistivity imaging tools. The proposed method utilizes a generator, global discriminator, and local discriminator to inpaint the missing regions of the image. The generator is based on an auto-encoder architecture with skip-connections, and the loss function used is the Wasserstein GAN loss. Our experiments on a dataset of borehole images demonstrate that the proposed model can effectively deal with large-scale missing pixels and generate realistic completion results. This approach can i…
Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal. However, current GAN-based generative inpainting models do not explicitly exploit the structural or textural consistency between restored contents and their surrounding contexts.To address this limitation, we propose to enforce the alignment (or closeness) between the local data submanifolds (or subspaces) around restored images and those around the original (uncorrupted) images during the learning process of GAN-based inpainting models. We exploit L…
University Pompeu Fabra（庞培法布拉大学）；Université de Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）；CNRS（法国国家科学研究中心）；University of Cambridge（剑桥大学）
Image inpainting refers to the restoration of an image with missing regions in a way that is not detectable by the observer. The inpainting regions can be of any size and shape. This is an ill-posed inverse problem that does not have a unique solution. In this work, we focus on learning-based image completion methods for multiple and diverse inpainting which goal is to provide a set of distinct solutions for a given damaged image. These methods capitalize on the probabilistic nature of certain generative models to sample various solutions that coherently restore the missing content. Along the …
Recent advances in image inpainting have shown impressive results for generating plausible visual details on rather simple backgrounds. However, for complex scenes, it is still challenging to restore reasonable contents as the contextual information within the missing regions tends to be ambiguous. To tackle this problem, we introduce pretext tasks that are semantically meaningful to estimating the missing contents. In particular, we perform knowledge distillation on pretext models and adapt the features to image inpainting. The learned semantic priors ought to be partially invariant between t…
Georgia Institute of Technology（佐治亚理工学院）；Apple（苹果公司）
Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image restoration under corruptions. However, the performance of GAN-inversion can be limited by a lack of robustness to unknown gross corruptions, i.e., the restored image might easily deviate from the ground truth. In this paper, we propose a Robust GAN-inversion (RGI) method with a provable robustness guarantee to achieve image restoration under unknown \texti…
Facial expression analysis in the wild is challenging when the facial image is with low resolution or partial occlusion. Considering the correlations among different facial local regions under different facial expressions, this paper proposes a novel facial expression restoration method based on generative adversarial network by integrating an improved graph convolutional network (IGCN) and region relation modeling block (RRMB). Unlike conventional graph convolutional networks taking vectors as input features, IGCN can use tensors of face patches as inputs. It is better to retain the structure…
Line Drawing Guided Progressive Inpainting of Mural Damage
United International College of Beijing Normal University - HongKong Baptist University（北京师范大学-香港浸会大学联合国际学院）；Wuhan University（武汉大学）；Cleveland State University（克利夫兰州立大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Image inpainting is a restoration method that reconstructs missing image parts. However, a carefully selected mask of known pixels that yield a high quality inpainting can also act as a sparse image representation. This challenging spatial optimisation problem is essential for practical applications such as compression. So far, it has been almost exclusively adressed by model-based approaches. First attempts with neural networks seem promising, but are tailored towards specific inpainting operators or require postprocessing. To address this issue, we propose the first generative adversarial ne…
Huya Inc（虎牙公司）；City University of Hong Kong（香港城市大学）；Hunan University（湖南大学）；Tencent AI Lab（腾讯AI实验室）
We propose PD-GAN, a probabilistic diverse GAN for image inpainting. Given an input image with arbitrary hole regions, PD-GAN produces multiple inpainting results with diverse and visually realistic content. Our PD-GAN is built upon a vanilla GAN which generates images based on random noise. During image generation, we modulate deep features of input random noise from coarse-to-fine by injecting an initially restored image and the hole regions in multiple scales. We argue that during hole filling, the pixels near the hole boundary should be more deterministic (i.e., with higher probability tru…
Restora-Flow: Mask-Guided Image Restoration with Flow Matching
Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz（格拉茨医科大学医学信息学、统计学与文献研究所）；Division of Medical Physics and Biophysics, Medical University of Graz（格拉茨医科大学医学物理与生物物理部）；Institute of Visual Computing, Graz University of Technology（格拉茨技术大学视觉计算研究所）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
Restereo: Diffusion stereo video generation and restoration
Max Planck Institute for Informatics（马克斯·普朗克信息研究所）；VIA-Center Saarbücken（萨尔布吕肯VIA中心）；University of Cambridge（剑桥大学）；Google（谷歌）；Google DeepMind（谷歌DeepMind）
TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models
College of Computer Science and Artificial Intelligence, Wenzhou University（温州大学计算机与人工智能学院）；University of British Columbia（不列颠哥伦比亚大学）；Nasdaq（纳斯达克）；Memorial University of Newfoundland（纽芬兰纪念大学）
Depth-Aided Color Image Inpainting in Quaternion Domain
Graduate School of Engineering Science, Osaka University（大阪大学工程科学研究生院）；Institute of Engineering, Tokyo University of Agriculture and Technology（东京农工大学工程学院）
Patch-to-Global: Random Patch Diffusion for Globally Consistent Megapixel Artifact Inpainting in Whole Slide Images
Korea University College of Medicine（高丽大学医学院）；Korea University Anam Hospital（高丽大学安岩医院）；Seegene Medical Foundation（Seegene医学财团）；Seoul National University（首尔大学）；Ewha Womans University（梨花女子大学）；The Catholic University of Korea（韩国天主教大学）
There are many methods for image enhancement. Image inpainting is one of them which could be used in reconstruction and restoration of scratch images or editing images by adding or removing objects. According to its application, different algorithmic and learning methods are proposed. In this paper, the focus is on applications, which enhance the old and historical scratched images. For this purpose, we proposed an adaptive spline interpolation. In this method, a different number of neighbors in four directions are considered for each pixel in the lost block. In the previous methods, predictin…
Indian Institute of Technology Gandhinagar（印度理工学院甘地讷格尔分校）
Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior, InGAN, SinGAN, and DCIL. These methods are unsupervised and are used to perform low-level vision tasks such as image restoration, image editing, and image synthesis. In this work, we proposed a new training data-independent framework, called Deep Contextual Features Learning (DeepCFL), to perform image synthesis and image restoration based on the semantics of the input image. The contextual features are simply the high dimensional vectors represen…
Australian AI Institute（澳大利亚人工智能研究所）；University of Technology Sydney（悉尼科技大学）
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-modal fusion model to integrate cross-modal features. However, these methods allocate most of the computation to visual encoding, while light computation on modeling modality interactions. Moreover, they take cross-modal fusion for depth features, which ignores a fine-grained alignment between text and image. Recently, vision-language pre-trained models (VLPM), encapsulating rich cross-modal alignment knowledge, have adv…
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting
University of South Carolina（南卡罗来纳大学）；Hong Kong Baptist University（香港浸会大学）；Cleveland State University（克利夫兰州立大学）；Center for Frontier AI Research (CFAR), A*STAR（新加坡科技研究局前沿人工智能研究中心（CFAR））
University of South Carolina（南卡罗来纳大学）；Tianjin University（天津大学）；Hong Kong Polytechnic University（香港理工大学）；Nanyang Technological University（南洋理工大学）
Although achieving significant progress, existing deep generative inpainting methods are far from real-world applications due to the low generalization across different scenes. As a result, the generated images usually contain artifacts or the filled pixels differ greatly from the ground truth. Image-level predictive filtering is a widely used image restoration technique, predicting suitable kernels adaptively according to different input scenes. Inspired by this inherent advantage, we explore the possibility of addressing image inpainting as a filtering task. To this end, we first study the a…
Zhejiang Sci–Tech University（浙江理工大学）；University of Alberta（阿尔伯塔大学）；University of Calgary（卡尔加里大学）
In this paper we are particularly interested in the image inpainting problem using directional complex tight wavelet frames. Under the assumption that frame coefficients of images are sparse, several iterative thresholding algorithms for the image inpainting problem have been proposed in the literature. The outputs of such iterative algorithms are closely linked to solutions of several convex minimization models using the balanced approach which simultaneously combines the $l_1$-regularization for sparsity of frame coefficients and the $l_2$-regularization for smoothness of the solution. Due t…
Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Research Institute of Sun Yat-sen University in Shenzhen（中山大学深圳研究院）；Afirstsoft
RISE Research Institutes of Sweden（瑞典RISE研究机构）；KTH Royal Institute of Technology（KTH皇家理工学院）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全研究中心）；Halmstad University（哈尔姆斯塔德大学）
University of Oxford（牛津大学）；Ludwig Institute for Cancer Research（路德维希癌症研究所）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院NHS基金会信托）
Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the automated analysis of endoscopy videos. Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem. Existing state-of-the-art methods only deal with the detection and…
Time-variant Image Inpainting via Interactive Distribution Transition Estimation
University of Alberta（阿尔伯塔大学）；IHPC and CFAR, A*STAR（IHPC和CFAR，A*STAR）；Nanyang Technological University（南洋理工大学）；University of South Carolina（南卡罗来纳大学）；School of Artificial Intelligence, Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, Jilin University（人工智能学院，知识驱动的人机智能工程研究中心，教育部，吉林大学）；College of Intelligence and Computing, Tianjin University（智能与计算学院，天津大学）；University of Tokyo（东京大学）
