ABSTRACT

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.

摘要

针对低照度或高粉尘浓度环境下煤矸石图像识别问题，提出一种基于改进Retinex算法的自适应增强方法：H-GF-MSR算法。该算法在HSV色彩空间中进行处理，在亮度分量上采用多尺度Retinex算法的引导滤波器，在饱和度分量上进行自适应饱和度拉伸。然后对图像进行直方图均衡处理，将两种算法融合以提高精度。与SSR算法、MSRCR算法等经典算法相比，该算法在抑制光晕产生和图像边缘增强方面有所改进。实验结果表明，在信息熵、图像均值等图像评价指标方面，图像平均梯度和峰值信噪比，与SSR算法相比，该算法分别提高了5.4%、41.1%、33.2%和0.4%；与MSRCR算法相比，平均提升分别为2.9%、32.4%、31.7%和0.7%。通过对10勒克斯～200勒克斯光照范围内煤矸石图像的增强实验，该算法具有较好的增强效果。能有效抑制强光，还原物体原有的细节。