Vimala Saravanan et.al./ REST Journal on Data Analytics and Artificial Intelligence, 4(3), September 2025, 1-11. Copyright@ REST Publisher 1 REST Journal on Data Analytics and Artificial Intelligence Vol: 4(3), September 2025 REST Publisher; ISSN: 2583-5564 Website: http://restpublisher.com/journals/jdaai/ DOI: https://doi.org/10.46632/jdaai/4/3/1 Advancements in Image Enhancement: Comparing Spatial and Frequency Domain Methods Using COPRAS Analysis *Vimala Saravanan, Vidhya Prasanth, M. Ramachandran, Chitra Periyasamy Rest Labs, Kaveripattinam, Krishnagiri, Tamilnadu, India *Corresponding Author Email: vimalarsri@gmail.com Abstract: 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. Keywords: Image enhancement, image smoothing, noise removal, nonlinear filtering. Adaptive filters, image enhancement, UN sharp masking 1. INTRODUCTION Numerous modern techniques for improving image quality and detecting edges share similarities with an early concept introduced by Dennis Gabor. Dennis Gabor, renowned for his groundbreaking work in optical holography, as well as the introduction of Gabor functions in communication theory, is most widely recognized for these contributions. His pioneering work served as the inspiration behind the development of Gabor filters, which have greatly advanced the field of image enhancement. Gabor filters, a group of linear filters, excel at eliminating textural elements and edge information from images. They find widespread application in computer vision and image processing tasks. Gabor filters allow for the enhancement of image textures and edges while suppressing noise and unimportant features by examining the frequency and orientation characteristics of different image regions. Consequently, they play a crucial role in various applications such as face recognition, texture analysis, and fingerprint recognition. Dennis Gabor's work has left a profound impact on image enhancement, providing a powerful tool for improving the quality of visual data and identifying vital features across a wide range of applications in image analysis and computer vision. [1] We evaluated the performance of this image enhancement approach by comparing the accuracy of an online fingerprint verification system with the quality index of the extracted minutiae. Our experimental results indicate that the application of the enhancement algorithm leads to increased verification accuracy and an improved quality index. Fingerprint