Low Light Vision Enhancement Using the Hazing Algorithm

Abstract

This journal publication presents a novel low light vision enhancement technique utilizing the Hazing algorithm. Low light conditions often pose significant challenges in various applications, such as surveillance, security, and outdoor imaging. The proposed technique aims to improve visibility and enhance the quality of low light images, thereby enabling better analysis and interpretation of visual information. The Hazing algorithm is a state-of-the-art method designed to address the limitations of traditional enhancement techniques in low light scenarios. It utilizes a combination of image dehazing and contrast enhancement algorithms to reduce haze, suppress noise, and enhance details. By leveraging the inherent characteristics of low light images, the Hazing algorithm effectively enhances image contrast, sharpness, and overall visual quality. The implementation of the technique involves a series of steps, including image acquisition under low light conditions, preprocessing to reduce noise and artifacts, application of the Hazing algorithm for enhancement, and post-processing to further refine the image quality. The proposed technique has been implemented and evaluated using a diverse set of low light images obtained from real-world scenarios. To assess the performance of the technique, several objective metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and visual quality assessment have been employed. The experimental results demonstrate that the Hazing algorithm effectively enhances low light images, yielding significant improvements in visibility and image quality. The proposed system offers a solution for processing and enhancing night vision images during criminal investigations. By utilizing the hazing algorithm and image processing techniques, the system improves visibility, enhances facial features, and facilitates accurate facial recognition. The implementation using MATLAB ensures efficient processing and analysis of low light images, thereby aiding law enforcement agencies in their efforts to investigate and solve criminal cases effectively.