Literature Review on Image Enhancement and Restorage in Dim Light-Captured Images

The pursuit of high-quality digital imaging faces significant challenges in low-light conditions, marked by diminished visibility, heightened noise, and a loss of intricate details. This paper explores the transformative role of contrast enhancement methods in overcoming these challenges. Beginning with an overview of the landscape, it delves into the effectiveness of histogram equalization (HE) methods, emphasizing their role in distributing pixel intensities and enhancing overall contrast. Additionally, adaptive frequency methods are proposed to specifically address low visibility, blurred details, and noise, offering a targeted solution for enhancing image quality in challenging lighting conditions. The survey of contrast enhancement methods extends to illumination-based enhancements, generative techniques, and their significance across domains such as surveillance, photography, and medical imaging. As technological advancements and ethical considerations shape the future, the integration of deep learning techniques holds promise for further breakthroughs in improving visibility, reducing noise, and restoring crucial details in dim light-captured images.