The low contrast and blurred details in subway tunnel images pose challenges in accurately extracting key environmental information during subsequent image processing. To solve this problem, this study proposes a blurred image restoration algorithm based on contrast stretching. First, conversion of the tunnel image to the Hue, Saturation, Value(HSV) color space occurs. Following this, a contrast stretching model tailored for the gray distribution of the V component enhances this component. This circumvents over-enhancement issues found in traditional low-illumination enhancement algorithms and allows for adaptive enhancement of image contrast. Subsequently, an analysis to identify the blur type in the enhanced V component of the tunnel image is conducted. Based on this analysis, division of the enhanced V component image into different regions occurs. In each region, selection of a qualified edge and estimation of its diffusion function takes place. Utilizing the point diffusion function as prior information, a non-blind deconvolution algorithm is applied to deburr each region. In the final step, fusion of the three components H, S, and V occurs, culminating in the overall enhancement and restoration of low-illumination tunnel environment images. Experimental results indicate that the proposed algorithm effectively enhances both overall and local contrast in tunnel images, reduces blur caused by Gaussian noise, and restores detail in the images
