EVALUATION OF PSNR VALUE FOR IMAGE SUPER-RESOLUTION USING DEEP LEARNING

Abstract

Deep learning is a branch of study that simulates the human brain and allows learning from large data. In recent years, deep learning techniques have become more prevalent in a range of applications. These techniques have been widely employed in image processing, especially for object detection, image augmentation, and super-resolution. Single-image super-resolution, which converts low-resolution images into high-resolution ones, is a significant application of deep learning. Outstanding perceptual and antagonistic outcomes have been achieved using multiple single-image super-resolution models. In this study, several noteworthy methods, including Super Resolution with Convolutional Neural Network, Super Resolution Residual Network, Super Resolution Generative Adversarial Network, and Enhanced Super Resolution Generative Adversarial Network are used for single image super-resolution. Each layer in these deep learning models carries out certain operations on low-resolution images to transform them into high-resolution equivalents. As a result, many high-resolution images can be produced from a single low-resolution image. We created a dataset of random images for our research. The original low-resolution images are compared to the high-resolution images that were created using a deep learning technique. Peak Signal-to-Noise Ratio and the Structural Similarity Index are used in the comparison. Additionally, the evaluation process includes computing the ground truth value. The highest similarity indexed is achieved by VGG54 with 96% of similarity.