Multi-Feature Fusion Algorithm in VR Panoramic Image Detail Enhancement Processing

Abstract

VR panoramic image is an image technology that covers a wide range of scenes. Its imaging range is much larger than that of traditional imaging systems, and it can fully reflect all the information of the imaging space. Although the multi-feature fusion method has been studied for a long time, the methods of multi-feature extraction, fusion and overall optimization have not been widely studied. In view of the shadow problem in VR panoramic images, this paper proposes a multi-feature fusion VR panoramic image shadow elimination algorithm, which uses HSV color features and LBP (Local Binary Pattern) / LSFP (Local Five Similarity Pattern) texture features to obtain shadow detection results and then obtains the final detection results by fusion. The experimental results prove that while ensuring a low missed detection rate, the false detection rate is greatly reduced. The comprehensive evaluation index Avg in this paper is improved by 3.4% compared with the shadow elimination algorithm based on a single feature. This paper proposes an image saliency detection algorithm and image detail enhancement algorithm based on multi-feature fusion. The final saliency map is obtained through linear fusion. Experiments prove that the image detail enhancement algorithm based on multi-feature fusion mentioned in this paper has achieved excellent results. In this paper, the performance of single feature fusion algorithm and multi-feature fusion algorithm are compared. The results show that the accuracy rate of multi-feature fusion algorithm based on HSV, LBP and LSFP is 93.39%, and the effect of multi-feature fusion is better than that of single feature.