An Empirical Study for Image Denoising and Image Quality Enhancement: A Challenging Overview

Noise removal means eliminating noise from a noisy image, thereby improving the quality of original image. Elimination of noise from the input signal remains a major issue for investigators. With the increasing number of digital images captured day-to-day, the requirement for more accurate perceptibly appealing image is enhancing. However, images taken by contemporary cameras are automatically deteriorated by noise, resulting in degraded visual quality. In general, retrieval of essential data from noisy images in the procedure of denoising to acquire the best quality of images is a big issue today. Therefore, work must be done to eliminate noise in the image without falling image characteristics, like edges, corners, and other sharp frameworks. Hence, this research paper overviews numerous techniques for image denoising and image quality enhancement. This overview investigates 50 Research papers related to noise removal and image quality enhancement, and developed technique-wise reviews, namely spatial domain-based techniques, optimization-based approaches, transform domain-based approaches deep learning (DL)-based techniques and machine learning (ML)-based techniques. An investigation participate in a survey based upon classifying experiment approaches, datasets, year of publication, toolset employed, effectual metrics for image denoising and image quality improvement. Finally, the challenges of overviewed techniques are illustrated to concentrate investigators for developing various efficient techniques for image denoising and image quality improvement.