Single Image Super Resolution Technique: An Extension to True Color Images

Muhammad Irfan,Arslan Arif,Muhammad Ismail,Aleem Khaliq,Sahib Khan,Zain Memon,Khalil Khan

doi:10.3390/sym11040464

Abstract

The super-resolution (SR) technique reconstructs a high-resolution image from single or multiple low-resolution images. SR has gained much attention over the past decade, as it has significant applications in our daily life. This paper provides a new technique of a single image super-resolution on true colored images. The key idea is to obtain the super-resolved image from observed low-resolution images. A proposed technique is based on both the wavelet and spatial domain-based algorithms by exploiting the advantages of both of the algorithms. A back projection with an iterative method is implemented to minimize the reconstruction error and for noise removal wavelet-based de-noising method is used. Previously, this technique has been followed for the grayscale images. In this proposed algorithm, the colored images are taken into account for super-resolution. The results of the proposed method have been examined both subjectively by observation of the results visually and objectively by considering the peak signal-to-noise ratio (PSNR) and mean squared error (MSE), which gives significant results and visually better in quality from the bi-cubic interpolation technique.

Highlights

There are numerous applications where high-resolution images are enviable like high-definition television broadcasting, surveillance system, sensor networks, and video conferencing, etc
The above algorithm is applied on different test images and compared with the bi-cubic interpolation technique
The above algorithm is images appliedwere on different images and The compared with the bi-cubic and qualitative judgment has been done

Summary

Introduction

There are numerous applications where high-resolution images are enviable like high-definition television broadcasting, surveillance system, sensor networks, and video conferencing, etc. It is not necessary that we can obtain the required high-resolution images due to certain limitations on resources, such as memory, power, bandwidth, and the cost of the camera. Several super-resolution techniques have been introduced and widely used to acquire the likely high-resolution images from the input, which will always be low-resolution images. The purpose of the image super-resolution technique is to use the low-resolution image to generate the high-resolution image, Symmetry 2019, 11, 464; doi:10.3390/sym11040464 www.mdpi.com/journal/symmetry. One image has been obtained from the multiple images of similar sights [1]. Multiple low-resolution images were used for the reconstruction of the high-resolution images. The new concepts of real-time image super resolution are in the evolving and developing stage. Sparsity-based techniques normally train a pair of dictionaries [2,3]

Methods

Results

Conclusion