Multi-focus image fusion method based on texture features extraction
To address the challenges associated with multi-focus image fusion,such as detail loss,edge artifacts,and block effects,we proposed a texture feature extraction-based method for multi-focus image fusion.The texture details were extracted from the image using a texture feature extraction algorithm,followed by enhancement and refinement through guided filtering.The initial decision map was first generated using a pixel maximum strategy on a filtered feature image,and then was refined to get a final decision map using a small region denoising method.Finally,a fully focused image was obtained by fusing the original image and the final decision map.The results show this method significantly improves the quality of the fused images.Compared with other methods,it reduces the mean square error evaluation value by about 37.14%,meanwhile the evaluation parameter values based on normalized mutual information metric,Tsallis entropy metric,and nonlinear correlation information entropy metric increase by about 26.1%,6.18%,and 13.2%,respectively.In addition,the method fully preserves image detail information without block effects or edge blur,which shows its significance in practical applications.
multi-focus image fusiontextural features extractionfeature enhancementguided filter
国家科技期刊平台
NSTL
万方数据
维普
