Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Abstract

In this paper, the Gram matrix is used to calculate the correlation of the filter response sets under different scale kernels learned by each layer of the network in the deconvolution, and the loss between the corresponding feature response correlations in the multilayer network is calculated. Linear summation is used to obtain a stable, multiscale image model representation. This paper extracts the contours of the salient areas of the image and adjusts the parameters of the deconvolution network to learn the salient area patterns of the image. At the same time, for the image to be generated, a shape template is used to limit the range of the area to be generated in order to obtain a shape image with similar patterns. When the spatial relative relationship characteristics of the image constituent objects are obvious, we appropriately add high-level semantic feature activation values for reinforcement. This paper solves the estimation of the unknown blur kernel by using image prior knowledge, filtering and gradient domain algorithms and other different technologies to obtain image jitter or scene movement information and estimate the size, location, and density of the blur kernel. This paper studies a relatively robust deconvolution model, which is insensitive to random noise, has stable effects, and can overcome the water ripple effect caused by the usual convolution process. This paper attempts to study the fuzzy model with variable space. The usual blur is a spatial invariant model; that is, a single kernel is used to describe the motion of all pixels on the image. By selecting different characteristic parameters, this paper conducts experimental research on some existing hydrophobic indicator function methods and calculates the relationship between characteristic parameters and hydrophobicity when different hydrophobic indicator functions are adopted. One characteristic of the hydrophobic image of composite insulators is low contrast. The traditional method of converting color images to grayscale images cannot improve the image contrast. This paper analyzes the hydrophobic image of the composite insulator, and the extracted B channel component image of the hydrophobic image improves the contrast of the image and facilitates the subsequent segmentation of water traces and background. In this paper, the water repellent image's watermark area is counted, and connected-domain wave processing is used to limit the area of water droplets retained, thereby improving the efficiency of filtering water droplets without having a big impact on the image as a whole. The problem of uneven illumination is an unavoidable problem in the field of image processing, and the resulting reflection problem brings difficulties to image processing. This article regards the reflective area of the watermark as a “hole” and uses the idea of “hole filling” to eliminate the reflective point, which weakens the reflection problem to a certain extent.

Design and Implementation of Transmission Line Insulator Online Monitoring Platform Based on Image Analysis

“…However, the digital image processing of the tested object and the estab-lishment of image eigenvalues and hydrophobicity grade criteria have not yet formed a mature and universal test method, and the test accuracy needs to be further improved. Therefore, it is necessary to study the detection technology of transmission line insulators, especially composite insulators [9][10][11][12].…”

On Edge Detection Algorithms for Water-Repellent Images of Insulators Taking into Account Efficient Approaches

“…Experimental results show 93.8% accuracy with 96.85% correct edge detection and 97.08% detection within three-pixel widths, demonstrating high accuracy for kernel SAR images. A deconvolution model that uses the Gram matrix to calculate filter response correlation and adjusts parameters to learn salient area patterns using shape templates has been proposed in [75]. It also estimates unknown blur kernels using image prior knowledge and gradient-domain algorithms.…”