Restoration Of Video Sequences Research Articles

Research on Error Concealment for Video Communication

The video sequence restoration described in this article has been extended to many areas of our lives today. People use it to perform various restoration tasks, making it possible for some pictures that were impossible to restore in the past. This paper uses experimental methods to study the error concealment and traditional image restoration in video compression, and mainly studies the CDD model algorithm, TV model algorithm and error mode. This article also has a very important meaning for the related research of video communication error control, that is, the research of digital image restoration technology, in the post-processing of film and television works, the fuzzification of network video, and the restoration of old documentaries.

A Survey on the methods of Super-resolution Image Reconstruction

Super-resolution (SR) image reconstruction is the process of combining several low resolution images into a single higher resolution image. There is a driving need for digital images of higher resolutions and quality. However, there is a limit to the spatial resolution that can be recorded by any digital device. Growing interest in super-resolution (SR) restoration of video sequences and the closely related problem of construction of SR still images from image sequences has led to the emergence of several competing SR reconstruction methodologies. In this paper, the principle of super-resolution image reconstruction and several state-of-the-art SR reconstruction methods were introduced. We critique these methods and at last, several aspects of super-resolution image reconstruction that should be studied further more were put forward.

A fast BNM (Best Neighborhood Matching): Algorithm and parallel processing for image restoration

AbstractBest Neighborhood Matching (BNM) algorithm is a good approach of error concealment in terms of restored image quality. However, this kind of error concealment algorithm is commonly computation‐intensive, which restricts their real applications on large‐scale image or video sequence restoration. In this article, we propose a fast method, named Jump and look around Best Neighborhood Matching (JBNM), which reduces computing time to one sixth of that by BNM, while the quality of the restored images remains almost the same. To further reduce processing time and meet large‐scale image restorations, a parallel JBNM working on a cluster of workstations is proposed. Several critical techniques, including reading policy, overlap stripe data distribution, and communication strategies, have been developed to obtain high performance. Both theoretical analysis and experiment results indicate that our parallel JBNM provides an efficient technique for image restoration applications. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 13, 189–200, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10057