at the problems of Poisson noise and uneven illumination in the visible light image of pantograph-catenary arcing, we propose an image processing algorithm including noise reduction and region segmentation. First, Poisson noise in pantograph-catenary arcing image is removed by two-dimensional Gabor wavelet transform algorithm, and then the illumination uniformity is judged according to the improved uniformity measurement algorithm. For the image with uneven illumination, two-dimensional Gamma function is used to segment. Simulation results show that the algorithm can effectively suppress Poisson noise, accurately segment the target area, and the segmentation accuracy reaches 95%. It provides an idea for quantitative analysis of intensity of pantograph-catenary arcing.

ing at the problems of information loss, unclear region and fog occlusion in foggy images, in this paper, a Gaussian pyramid transform Retinex image enhancement algorithm based on bilateral filtering is proposed to improved the contrast of foggy image. First, an improved mathematical model of bilateral filtering function based on spatial kernel function and pixel difference is established. Second, the input image is converted into HSI (hue, Saturation, intensity) image, and the improved bilateral filtering function is used to replace the Gaussian function in the original algorithm. The reflection component is extracted from the luminance image (I color space) to obtain a reflection image with edges retained and unaffected by brightness. Finally, color images of different scales are obtained through Gaussian pyramid down sampling, the images of different scales are enhanced by improved Retinex algorithm, and the image is reconstructed based on Gaussian Laplacian algorithm to improve the image contrast. Experimental results show that the algorithm can effectively enhance the contrast of the image, and the color of the processed image conforms to the observation effect of human eyes.

ing at the problems that the classical bilateral filtering algorithm has a poor effect on depth image repair and cannot adjust the kernel function parameters accurately, a depth image repair algorithm based on morphology and improved bilateral filtering is proposed. First, the morphological algorithm is used to optimize the holes in the depth image to fill some small holes and filter out random noise. Then, using the improved bilateral filtering algorithm, the probability distribution function and the maximum likelihood function are introduced to calculate the kernel function parameter values in the neighborhood of each cavity and thus to adjust the kernel function parameter adaptively and realize the repair of large area holes. Finally, the median filter algorithm is used to smooth the image and thus to remove the "burr" of the depth image, and the edge details of the image are retained and the sharpness is also maintained. The experimental results show that the proposed algorithm can effectively fill the holes in the depth image without losing the original depth image information, can realize the goal of edge preservation and denoising, and has strong robustness.

ing at the problem of low matching accuracy and matching failure when using point features or line features alone in remote sensing images, a fully automatic registration algorithm for remote sensing images incorporating point and line complementary features is proposed. First, the improved scale-invariant feature transform (SIFT) algorithm is used to obtain the initial matching points, and the normalized cross-correlation (NCC) measure and the random sampling consistency algorithm are used to eliminate possible mismatches to obtain the points with the same name with high accuracy. Then, an improved line segment detection operator (LSD) is used to extract line segment features, determine candidate matching line segments and construct feature descriptors by known homography geometric transformation constraints and slope constraints, and then obtain line segments with the same name. Finally, the intersection point of the line segments with the same name is extracted, and the same-named point set of the first step is integrated to calculate the projection transformation parameters between the images to realize the image registration. Experimental results show that the proposed algorithm has significant advantages in matching accuracy and matching accuracy.

detection in complex environment is affected by many factors. Traditional robust principal component analysis (RPCA) fails to obtain the lowest rank representation from disturbed data. Therefore, a novel method of video denoising and object detection algorithm based on RPCA model with l₁-total variational (TV) regularization constraints is proposed. Based on RPCA, under the framework of low-rank sparse decomposition, the low-rank nature of the nuclear norm is used to model the background, and the three-dimensional TV combined with l₁ norm regularization to constrain the sparsity and spatial continuity of the foreground object, and then l₂ norm regularization is combined to constrain the noise part so as to make up for the deficiencies of the existing RPCA model. Using alternating iteration method, augmented Lagrange multiplier method is used to optimize the objective function, and the denoising and target detection in complex environment are realized. Experimental results show that the method can not only accurately detect moving objects under noise interference, but also maintain a relatively fast running speed, which provides a reference for the real-time detection of video. Compared with other similar methods, it not only has better detection effect, but also has advantages in the three indicators of quantitative evaluation.