Multifocus Fusion Image Enhancement Based on Image Subtraction Angiography and NSML

With the aim of denoising the results of the existing image-fusion algorithms and making them more uniform with respect to quality, we propose a fusion image enhancement method. First, the source image is meanfiltered and salient arca of the target image is obtained using the digital subtraction technology. The subtracted image is then decomposed in two-scale using an improved Laplacian operator to obtain the corresponding coarse and refined focus areas. Further, an initial decision graph is generated according to the pixel -level linear mixing rules, and the final decision graph is obtained by refining the initial decision graph using the consistency check algorithm. Finally, the results arc synthesized to reconstruct a new fusion image. Experimental results show that the proposed method achieves different degrees of enhancements of the fusion image generated using the existing fusion algorithms, the image has improved robustness to noise, and processing time is less than 0.1 s. The small defocus or focus area in the fusion image is more. With good recognition ability, the edge information of recognition increases in clarity and smoothness, and specific verification results arc given for objective indicators.