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

Best 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.