Coal mine underground image enhancement method based on dust removal estimation and multiple exposure fusion

Factors such as dust and dim light in coal mines lead to low quality of collected images. The existing image enhancement methods have problems such as loss of image details, unclear local features, inability to eliminate noise, and unsatisfactory dust removal effects. In order to solve the above problems, a coal mine underground image enhancement method based on dust removal estimation and multiple exposure fusion is proposed. This method uses a simplified model of dust image and dark primary color theory, and introduces an adaptive attenuation coefficient to estimate the image transmittance. Based on the transmittance distribution, the original image of the object is restored using the simplified model of dust image to remove dust from the coal mine underground image. The method uses a multiple exposure fusion algorithm to generate a set of images with different exposure ratios for underexposed original images, and introduces a weight matrix to fuse these images with the original image, effectively improving the quality of dim light images. The experimental results show that compared to the histogram equalization method, the multiple-scale Retinex with color restoration method (MSRCR), and the improved Retinex method, this method has better results in dust removal and dim light enhancement, with higher color restoration, suppressed white edges and overexposure. The average contrast of the enhanced images has increased by 62.78%, 29.82%, 9.8%, and the average image entropy has increased by 34.13%, 14.12%, and 8.25%, respectively. The average lightness order error (LOE) has been reduced by 40.9%, 20.39%, and 8.5%, respectively. This method has the shortest computational time.