Deblurring image compression algorithm using deep convolutional neural network - Menassel, Rafik; Gattal, Abdeljalil; Kerdoud, Fateh (Institute of Advanced Engineering and Science, 2024)
There are instances where image compression becomes necessary; however, the use of lossy compression techniques often results in visual artifacts. These artifacts typically remove high-frequency detail and may introduce noise or small image structures. To mitigate the impact of compression on image perception, various technologies, including machine learning and optimization metaheuristics that optimize the parameters of image compression algorithms, have been developed. This paper investigates the application of convolutional neural networks (CNNs) to reduce artifacts associated with image compression, and it presents a proposed method termed deblurring compression image using a CNN (DCI-CNN). Trained on a UTKFace dataset and tested on six benchmark images, the DCI-CNN aims to address artifacts such as block artifacts, ringing artifacts, blurring artifacts, color bleeding, and mosquito noise. The DCI-CNN application is designed to enhance the visual quality and fidelity of compressed images, offering a more detailed output compared to generic and other deep learning-based deblurring methods found in related work.