![]() ![]() Contrary to the existing recovery methods, we aim to divide the captured image into the illumination image as well as the reflectance image and only estimate the illumination one, where the enhanced map of the low-light image is acquired by using the retinex theory. In this paper, we propose a lightweight stereo visual odometry system for navigation of autonomous vehicles in low-light situations. Although numerous works focused on this issue, it still has a number of inherent drawbacks. The main reason for this challenge is the blurred images in the scenes with insufficient illumination. ![]() In the low-light environments, it is difficult to navigate independently using a visual odometry for autonomous driving. Localization of vehicles in a 3D environment is a basic task in autonomous driving. Extensive experiments along with qualitative and quantitative evaluations show that the performance of AGC is better than other state-of-the-art techniques. Afterwards, an adaptive gamma correction (AGC) is proposed to appropriately enhance the contrast of the image where the parameters of AGC are set dynamically based on the image information. Hence, we classify images into several classes based on the statistical information of the respective images. These methods do not consider the nature of the image, whereas different types of degraded images may demand different types of treatments. Most of the existing methods mainly focus on either global or local enhancement that might not be suitable for all types of images. In spite of much advancement in imaging science, captured images do not always fulfill users’ expectations of clear and soothing views. Due to the limitations of image-capturing devices or the presence of a non-ideal environment, the quality of digital images may get degraded. ![]()
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