Research on image enhancement processing algorithms in computer art education

This research paper explores the application of image enhancement processing algorithms in the context of computer art education. The paper acknowledges the significance of high-quality images in art education and addresses the challenges in enhancing image quality due to factors like equipment performance, environmental conditions, and photographic techniques. It delves into various image enhancement methods, such as those based on physical models for dehazing and underwater image enhancement, including the dark channel prior theory and methods that do not require prior knowledge of camera parameters. The study also covers the importance of objective and precise evaluation of image quality changes before and after processing, highlighting the necessity for reliable image quality evaluation methods. It introduces the Unsupervised Low-Light Enhancement Algorithm (ULEA) based on attention mechanisms, detailing its network structure, loss functions, and the impact of these functions on low-light image enhancement processing. Through experimental research and comparative analysis, the paper demonstrates the effectiveness of ULEA in enhancing low-light images, significantly improving overall brightness and image texture layers compared to traditional and deep learning-based methods. In conclusion, the paper presents a compelling case for the practical application of advanced image enhancement algorithms in computer art education, proving their effectiveness in enhancing the visual quality of images and their applicability in a variety of conditions.