Research on Local Feature Intelligent Extraction Algorithm of Blurred Image Under Complex Illumination Conditions

Abstract

Traditionally, multi-label feature extraction algorithm is used to extract local features of blurred images under complex illumination conditions. The computational complexity is extremely high and the extraction efficiency is low. Therefore, in this paper, the local feature intelligent extraction algorithm for blurred images under complex illumination conditions is proposed. The improved image segmentation algorithm based on local fuzzy C-means clustering is used to cluster and segment the blurred images. The image feature recognition algorithm based on wavelet transform and LBP log domain feature extraction is adopted. The blurred image is transformed from the spatial domain to the logarithmic domain to make two-stage discrete wavelet decomposition, and the high-frequency component is used to reconstruct the original image to perform high-pass filtering on the blurred image. By filtering the low-frequency illumination component to compensate the complex illumination, the block LBP is used to extract the local texture features of the blurred image after illumination compensation. The experimental results on Yale-B face database show that the proposed algorithm can effectively extract the local features of blurred images under complex illumination conditions, and the maximum robustness of the algorithm is 0.44, the maximum value of feature extraction error rate is 0.25, and the maximum value of feature extraction speed growth rate is 96%, with high robustness, accuracy and extraction efficiency.