A mine image enhancement method based on structural texture decomposition

Abstract

There is a phenomenon of low lighting and excessive dust in underground mines, which leads to uneven lighting, blurriness, and loss of details in the images captured by monitoring videos. It affects subsequent intelligent image recognition. Existing mine image enhancement methods generally suffer from unclear texture details and poor visual effects. A method for image enhancement based on structural texture decomposition is proposed. Firstly, the maxRGB algorithm is used to extract the initial lighting component from the original image. Then, an optimization objective function is constructed to sequentially optimize and solve the structural component, texture component, and noise component in the initial lighting component. The weighted guided filtering is applied to the initial lighting component as a prior constraint, and the structural component with clear edges is obtained iteratively. Combined with the maximum neighborhood difference method and weighted average local variation, a local variation deviation function is constructed as constraint weights. The texture component with rich details is obtained iteratively. Secondly, the original image is transformed into the HSV color space, and the lighting component of the original image is extracted. Combined with the structural component, texture component, and noise component, Retinex theory is used for reconstruction to obtain the enhanced initial lighting component. To avoid excessive lighting enhancement, adaptive Gamma correction with weight distribution (AGCWD) is introduced to process the initial lighting information of the image and obtain the final lighting component. Finally, the image is converted to RGB color space to obtain an enhanced image. The experimental results show the following points. ① The image enhancement algorithm based on structural texture decomposition can ensure clearer texture details at the edges of the image, reduce halo artifacts during the image enhancement process, and achieve a more balanced grayscale histogram of the enhanced image. ② Compared with five image enhancement algorithms, including the Retinex algorithm based on structure and texture aware Retinex(STAR), the joint intrinsic-extrinsic prior model (JieP), the weighted variational mode (WVM), the semi-decoupled decomposition (SDD), and multi-scale Retinex with color restoration (MSRCR), the natural image quality evaluator (NIQE) of the image enhancement algorithm based on structural texture decomposition is reduced by 8.69%, 29.05%, 11.2%, 29.53%, and 33.54%, respectively. The visual information fidelity (VIF) increases by 91.17%, 117.86%, 59.38%, 48.78%, 183.12%, and the entropy index (Entropy) increases by 3.20%, 8.02%, 4.07%, 3.49%, and 22.68%, respectively. ③ The image enhancement algorithm based on structural texture decomposition has a running time only longer than the MSRCR algorithm. But the enhancement effect is better, which can meet the needs of image enhancement in underground mines.

Keywords

• mine image enhancement
• structural texture decomposition
• variational model
• retinex theory
• adaptive gamma correction