An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution

TLDR

Image super‑resolution, traditionally driven by optimization‑based methods, has recently attracted attention from AI and deep‑learning approaches, yet many studies still rely on mean‑squared error metrics that overlook perceptual quality. This paper presents a comparative analysis of efficient techniques for image super‑resolution. Keywords: image super‑resolution, AI, machine learning, deep learning, performance.

Abstract

The image up scaling and super resolution gain a attention now a days. Create a high-resolution image with help of a low-resolution image uses in many of the application. The optimization based super-resolution methods is principally driven by the choice of the objective function. Recently artificial intelligence based machine and deep learning methods are also using for the image processing application. It makes easy and efficient way of image super resolution. Recent work has generally centered around limiting the mean squared reproduction blunder. The subsequent evaluations have high pinnacle signal-tocommotion proportions, however they are much of the time lacking high-recurrence subtleties and are perceptually subpar as in they neglect to match the constancy expected at the higher resolution. This paper presents the comparative analysis of the efficient techniques for image super-resolution. Keywords—Image, Super, Resolution, AI, Machine learning, Deep Learning, Perfromance.