Advancements in Image Enhancement Comparing Spatial and Frequency Domain Methods Using COPRAS Analysis

Image enhancement is a fundamental process in the field of computer vision and digital image processing, aimed at improving the quality and visual appeal of images for various applications. This abstract provides an overview of key concepts, techniques, and applications in image enhancement. Image enhancement techniques can be broadly categorized into two main approaches: spatial domain and frequency domain methods. In the spatial domain, image enhancement is performed directly on the pixel values of the image. Common spatial domain techniques include contrast adjustment, brightness correction, histogram equalization, and filtering operations like sharpening and smoothing. These methods are effective for enhancing image details and reducing noise. Frequency domain techniques. In the frequency domain, it becomes possible to manipulate the image's frequency components, allowing for operations like filtering out specific frequencies to remove noise or enhance certain features. Frequency domain methods are particularly useful in applications like image denoising and compression. Image enhancement is a critical area of research in various fields, including computer vision, image processing, and computer graphics. Its significance stems from its ability to improve the visual quality of images and make them more suitable for various applications. In the field of medical imaging, image enhancement plays a vital role in improving the visibility of important details in X-rays, MRIs, and other medical images. This can aid in the early detection of diseases and enhance the accuracy of medical diagnoses. The COPRAS-G method requires identifying selection criteria; evaluating information related to these criteria, and developing methods to evaluate Meeting the participant's needs Criteria for doing in order to assess the overall performance of the surrogate. Decision analysis involves a Decision Maker (DM) Situation to do consider a particular set of alternatives and select one among several alternatives, usually with conflicting criteria. For this reason, the developed complexity proportionality assessment (COPRAS) method can be used. From the result Red channel is got the first rank whereas the fusion based is having the lowest rank.