Photo defect detection for image inpainting

[[abstract]]Image inpainting (or image completion) techniques use textural or structural information to repair or fill damaged portion of a picture. However, most techniques request a human to identify the portion to be inpainted. We developed a new mechanism which can automatically detect defect portions in a photo, including damages by color ink spray and scratch drawing. The mechanism is based on several filters and structural information of damages. Old photos from the author's family are used for testing. Preliminary results show that most damages can be automatically detected without human involvement. The mechanism is integrated with our inpainting algorithms to complete a fully automatic photo defects repairing system.[[conferencetype]]國際[[conferencedate]]20051212~20051214[[conferencelocation]]Irvine, CA, US

[[alternative]]Photo Defect Detection and Inpainting

計畫編號：NSC94-2213-E032-017研究期間：200508~200607研究經費：398,000[[sponsorship]]行政院國家科學委員

A knowledge based architecture for the virtual restoration of ancient photos

Historical images are essential documents of the recent past. Nevertheless, time and bad preservation corrupt their physical supports. Digitization can be the solution to extend their \u201clives\u201d, and digital techniques can be used to recover lost information. This task is often difficult and time-consuming, if commercial restoration tools are used for the purpose. A new solution is proposed to help non-expert users in restoring their damaged photos. First, we defined a dual taxonomy for the defects in printed and digitized photos. We represented our restoration domain with an ontology and we created some rules to suggest actions to perform in case of some specific events. Classes and properties of the ontology are included into a knowledge base, that grows dynamically with its use. A prototypal tool and a web application version have been implemented as an interface to the database, and to support non-expert users in the restoration process

Detection and Removal of Long Scratch Lines in Aged Films

[[abstract]]Historical films usually have defects. We study the type of defects, and propose a series of solutions to detect defects before they are repaired by our inpainting algorithms. This paper focuses on a difficult issue to locate long vertical line defects in aged films. A progressive detection algorithm is proposed. We are able to detect more than 86% (recall rate) of effective line defects. These line defects are then removed step by step. The experiments use real historical video collected from national museum and public channel, instead of using computer generated noise. The results are visually pleasant based on our subjective evaluation by volunteers[[conferencetype]]國際[[conferencedate]]20060709~20060712[[iscallforpapers]]Y[[conferencelocation]]Toronto, Ont., Canad

AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models

Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have demonstrated the capability of understanding images and achieved remarkable performance in various visual tasks. Despite their strong abilities in recognizing common objects due to extensive training datasets, they lack specific domain knowledge and have a weaker understanding of localized details within objects, which hinders their effectiveness in the Industrial Anomaly Detection (IAD) task. On the other hand, most existing IAD methods only provide anomaly scores and necessitate the manual setting of thresholds to distinguish between normal and abnormal samples, which restricts their practical implementation. In this paper, we explore the utilization of LVLM to address the IAD problem and propose AnomalyGPT, a novel IAD approach based on LVLM. We generate training data by simulating anomalous images and producing corresponding textual descriptions for each image. We also employ an image decoder to provide fine-grained semantic and design a prompt learner to fine-tune the LVLM using prompt embeddings. Our AnomalyGPT eliminates the need for manual threshold adjustments, thus directly assesses the presence and locations of anomalies. Additionally, AnomalyGPT supports multi-turn dialogues and exhibits impressive few-shot in-context learning capabilities. With only one normal shot, AnomalyGPT achieves the state-of-the-art performance with an accuracy of 86.1%, an image-level AUC of 94.1%, and a pixel-level AUC of 95.3% on the MVTec-AD dataset. Code is available at https://github.com/CASIA-IVA-Lab/AnomalyGPT.Comment: Project page: https://anomalygpt.github.i

Deep Industrial Image Anomaly Detection: A Survey

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection

Removal of visual disruption caused by rain using cycle-consistent generative adversarial networks

This paper addresses the problem of removing rain disruption from images without blurring scene content, thereby retaining the visual quality of the image. This is particularly important in maintaining the performance of outdoor vision systems, which deteriorates with increasing rain disruption or degradation on the visual quality of the image. In this paper, the Cycle-Consistent Generative Adversarial Network (CycleGAN) is proposed as a more promising rain removal algorithm, as compared to the state-of-the-art Image De-raining Conditional Generative Adversarial Network (ID-CGAN). One of the main advantages of the CycleGAN is its ability to learn the underlying relationship between the rain and rain-free domain without the need of paired domain examples, which is essential for rain removal as it is not possible to obtain the rain-free image under dynamic outdoor conditions. Based on the physical properties and the various types of rain phenomena [10], five broad categories of real rain distortions are proposed, which can be applied to the majority of outdoor rain conditions. For a fair comparison, both the ID-CGAN and CycleGAN were trained on the same set of 700 synthesized rain-and-ground-truth image-pairs. Subsequently, both networks were tested on real rain images, which fall broadly under these five categories. A comparison of the performance between the CycleGAN and the ID-CGAN demonstrated that the CycleGAN is superior in removing real rain distortions