Research on low illumination coal gangue image enhancement based on improved Retinex algorithm

• Published in: International journal of coal preparation and utilization Vol. 43; no. 6; pp. 999 - 1015
• Main Authors: Shang, Deyong, Yang, Zhiyuan, Zhang, Xi, Zheng, Linlin, Lv, Zhibin
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.06.2023; Taylor & Francis Ltd
• Subjects: Algorithms; Coal; coal gangue recognition; Entropy (Information theory); Gangue; guided filtering; Illumination; Image enhancement; Low illumination; Retinex algorithm; Signal to noise ratio
• ISSN: 1939-2699; 1939-2702
• DOI: 10.1080/19392699.2022.2089129

Aiming at the problems of coal and gangue image recognition in low illumination or high dust concentration environment, an adaptive enhancement method based on improved Retinex algorithm, is proposed: H-GF-MSR algorithm. The algorithm is processed in HSV color space, uses guided filter for multi-scale Retinex algorithm in brightness component, and carries out adaptive saturation stretching in saturation component. Then the images are processed by histogram equalization, and the two algorithms are fused to improve the accuracy. Compared with SSR algorithm, MSRCR algorithm and other classical algorithms, this algorithm is improved in suppressing the generation of halo and image edge enhancement. The experimental results show that in terms of image evaluation indexes such as information entropy, image mean, image average gradient and peak signal-to-noise ratio, compared with SSR algorithm, the algorithm improves by 5.4%, 41.1%, 33.2% and 0.4% respectively; compared with MSRCR algorithm, the average improvement is 2.9%, 32.4%, 31.7% and 0.7% respectively. Through the enhancement experiments of coal gangue image in the illumination range of 10 lux ~ 200 lux, the algorithm has better enhancement effect. It can effectively suppress the bright light and restore the original details of the object.