Abstract: In order to solve the problem of limited retention of detail information in the multi-focus image fusion algorithm, a multi-focus image fusion algorithm with improved sparse representation and integrated energy sum is proposed. Firstly,the non-subsampled shearlet transform is used on the source image to obtain low-frequency and high-frequency coefficient matrix. Secondly, the image block is extracted from the low-frequency coefficient matrix through the sliding window technique,a joint local adaptive dictionary is constructed, and the sparse representation coefficients are calculated using the orthogonal matching tracking algorithm. Then, the sparse after fusion is obtained using the variance energy weighting rule coefficients, and the fused low-frequency coefficient matrix is obtained through the reverse sliding window technique. Thirdly, for the high-frequency coefficients, the integration rule of the integrated energy sum is proposed to obtain the fused high-frequency coefficient matrix. Finally, the fusion image is obtained by inverse transformation. The experimental results show that the algorithm can retain more detailed information and has certain advantages in visual quality and objective evaluation.

Key words: multi-focus image fusion, non-subsampled shearlet transform, improved sparse representation, integrated energy sum