Affiliation:
1. Department of Intelligent Robot Engineering, Pukyong National University, Busan 48513, Republic of Korea
2. School of Electrical Engineering, Pukyong National University, Busan 48513, Republic of Korea
Abstract
Images acquired in low-light conditions often have poor visibility. These images considerably degrade the performance of algorithms when used in computer vision and multi-media systems. Several methods for low-light image enhancement have been proposed to address these issues; furthermore, various techniques have been used to restore close-to-normal light conditions or improve visibility. However, there are problems with the enhanced image, such as saturation of local light sources, color distortion, and amplified noise. In this study, we propose a low-light image enhancement technique using illumination component estimation and a local steering kernel to address this problem. The proposed method estimates the illumination components in low-light images and obtains the images with illumination enhancement based on Retinex theory. The resulting image is then color-corrected and denoised using a local steering kernel. To evaluate the performance of the proposed method, low-light images taken under various conditions are simulated using the proposed method, and it demonstrates visual and quantitative superiority to the existing methods.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference25 articles.
1. Low-light image enhancement via deep Retinex decomposition and bilateral learning;Lv;Signal Process. Image Commun.,2021
2. Modified gaussian filter based on fuzzy membership function for awgn removal in digital images;Cheon;J. Inf. Commun. Converg. Eng.,2021
3. Dai, Q., PU, Y.F., Rahman, Z., and Aamir, M. (2019). Fractional-order fusion model for low-light image enhancement. Symmetry, 11.
4. Mittal, A., Moorthy, A.K., and Bovik, A.C. (2022, January 22–24). Improved Retinex for low illumination image enhancement of nighttime traffic. Proceedings of the 2022 International Conference on Computer Engineering and Artificial Intelligence, Shijiazhuang, China.
5. Mukaida, M., Ueda, Y., and Suetake, N. (2022, January 16–19). Low-light image enhancement method by using a modified gamma transform for convex combination coefficients. Proceedings of the 2022 IEEE International Conference on Image Processing, Bordeaux, France.
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