Paper Key : IRJ************047
Author: Ravi
Date Published: 04 Dec 2024
Abstract
Low-light environments often degrade image quality, posing challenges in applications like surveillance and autonomous driving. This paper presents a novel approach combining Convolutional Neural Networks (CNN) with a pyramid-based fusion technique to enhance low-light images. Our method leverages deep learning for feature extraction and multi-scale fusion to preserve fine details and improve illumination. Comparative experiments demonstrate superior performance over existing state-of-the-art methods.. This research introduces an innovative approach to enhance the quality of nighttime roadside images, crucial for intelligent transportation systems. Current methods for improving low-light photos often result in color abnormalities and other issues. Our solution addresses these challenges by incorporating multiple sensors and techniques. Instead of conventional methods, we utilize a novel approach called bidirectional area segmentation-based inverse tone mapping to enhance photos. Additionally, we tackle the problem of moving objects appearing dim by employing a unique highlighting method based on precise identification of moving objects in the image data. Ultimately, we generate high-quality traffic photos using a pyramid-based fusion approach. Experiments with various images demonstrate that our technique outperforms existing methods in enhancing image details and creating more realistic colors for improved human observation.