Clarity Method of Low-illumination and Dusty Coal Mine Images Based on Improved Amef

Chang Su, Ziqiang Li, Zhongliang Wei, Naizhong Xu, Quan Yuan


The existing most image processing methods based on physical models can have a significant impact on defogging performance due to inaccurate estimation of the depth of field information. These methods often encounter problems such as low brightness, invisible color distortion, and loss of detail when processing images with poor lighting conditions, such as those taken in coal mines. To address these issues, this paper proposes a new algorithm based on artificial multi-exposure image fusion. The proposed method performs global exposure on images with uneven illumination by combining S-type functions and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm in the Hue-Saturation-Value (HSV) color space. This reduces the spatial dependence of brightness during processing and avoids color distortion problems that may arise in the Red-Green-Blue (RGB) color space. To mitigate the issue of detail loss, a gradient-domain guided filter is used to preserve fine structures in images, while an improved homomorphic filtering algorithm is introduced during the Laplacian pyramid decomposition process to reduce image content loss arising from large dark areas. This paper also conducted subjective, objective, and computational time comparisons to evaluate performance, providing reliable results regarding speed, quality, and reliability in processing hazy images.

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