A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework  

作　　者：Mahmoud M.Khattab Akram M.Zeki Ali A.Alwan Belgacem Bouallegue Safaa S.Matter Abdelmoty M.Ahmed 

机构地区：[1]Faculty of Information and Communication Technology,International Islamic University Malaysia,Kuala Lumpur,Malaysia [2]College of Computer Science,King Khalid University,Abha,Saudi Arabia [3]School of Theoretical&Applied Science,Ramapo College of New Jersey,Rampao Valley Road,Mahwah,USA [4]Community College,King Khalid University,Abha,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第1期35-54,共20页计算机系统科学与工程（英文）

基　　金：the Institute for Research and Consulting Studies at King Khalid University through Corona Research(Fast Track)[Grant Number 3-103S-2020].

摘　　要：The prime purpose for the image reconstruction of a multi-frame super-resolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images,which is useful in numerousfields.Nevertheless,super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts,which include blurring distortion,noises,and stair-casing effects.Consequently,it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image.In this research work,we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception,which improves human analysis and interpretation processes.Accordingly,we propose a new approach to the image reconstruction of multi-frame super-resolution,so that it is created through the use of the regularization framework.In the proposed approach,the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image,including sharp image edges and texture details while preventing artifacts.The experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.

关 键 词：SUPER-RESOLUTION regularized framework bilateral total variation bilateral edge preserving 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]