Open Access

ARTICLE

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

Mahmoud M. Khattab^1,2,*, Akram M Zeki¹, Ali A. Alwan³, Belgacem Bouallegue², Safaa S. Matter⁴, Abdelmoty M. Ahmed²
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
* Corresponding Authors: Mahmoud M. Khattab. Email: ,

Computer Systems Science and Engineering 2023, 44(1), 35-54. https://doi.org/10.32604/csse.2023.025251

Received 20 November 2021; Accepted 20 December 2021; Issue published 01 June 2022

Abstract

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 numerous fields. 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.

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Keywords

Super-resolution; regularized framework; bilateral total variation; bilateral edge preserving

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