Enhance Image Quality with CAS-CNN: A Deep Convolutional Neural Network for Compression Artifact Suppression

Abstract

Objective: The goal is to outdo clumsy filtering algorithms and prevailing deep learning approaches in terms of the image quality metrics, computational efficiency, and memory usage by attaining better performance. Methodology: Two-step approach has the methodology component including the formulation of the problem and review of the literature collections to detect known techniques. CAS-CNN's architecture is designed, and the entire experiment setup requires the preparation of an original and a compressed images dataset and also defining loss function for training and improving CAS-CNN parameters as a whole. Outputs of metrics are the PSNR, SSIM, MOS values, inference time, model size, training time, and hardware usage. Results and Discussion: In terms of competitiveness, CAS-CNN is always ahead of all the latest algorithm levels and achieves the best results by standard metrics such as PSNR and SSIM, thus it is the method that gives the most undistorted recreated images with a high quality. It has shown its ability to compete closely with traditional methods by providing better performance, as well as consuming fewer computing resources, which makes it closer to the practical reality of real time applications. The CAS-CNN's model creates efficiency that enables cutting-edge design and testing into the market timelier than the existing methods do. Small progress is seen despite its quick performance; but still, important issues like artificial data and restrictions on given conditions are noticed, which means that we must not stop searching for scaling and generalizing power. Conclusion: CAS-CNN is a next-level algorithm concerning removing distortion in compression, superior to prevailing techniques in terms of both effectiveness, efficiency, and accessibility. It holds an edge over other methods due to its high performance and efficiency, and as such, it is an applicable tool for a variety of situations where one must deal with limited resources in the need for high-quality images. More research is needed to account for the existing shortcomings and apply CAS-CNN to a range of different datasets, as well as the circumstances in the real world.