Analysis of Image Enhancement and Deep Learning Techniques

Image analysis is a basic step of transforming an image by extracting meaningful information from digitally stored images through certain functions to improve the quality of image for further applications. These transformations include feature extraction, edge detection, segmentation, filtering, compression, recognition, denoising, image inpainting, and pixel-level enhancement, etc. In this paper, some standard methods of image enhancement are discussed and compared with deep learning techniques, which are the recent areas of research in the field of image processing as well as in computer vision and medical image processing. The paper provides a comprehensive overview of standard image processing techniques and deep learning-based techniques along with their merits and demerits. Deep learning techniques have some exclusive features like less requirement of pre-processing of data, high-dimensional feature extraction, improved accuracy, and better image quality as compared to standard image processing techniques. As deep learning techniques are commonly used as image generation techniques and performing well in medical imaging, character generation, text to speech generation and helpful in identifying fake data. These techniques can improve the creativity by generating new images for interior designing of homes and office spaces as well as the designing of fabrics, automobiles.