A novel multi-focus image fusion method using multiscale shearing non-local guided averaging filter

The multiscale transform based image fusion method cannot effectively preserve detail information and easily produce artifacts. Faced with these problems, we present a novel multi-focus fusion method based on multiscale shearing non-local guided averaging filter (MSNLGA). First, we construct a new multiscale geometrical analysis tool called MSNLGA, which combines the non-local guided averaging filter with the shearing filter bank. The MSNLGA can represent the intrinsic geometric structure of image sparsely because of its good property in multiscale, multi-direction and shift-invariance. Then, the MSNLGA is used to decompose source images to obtain approximate subbands and directional detail subbands. For the approximate subbands, we extract the anti-noise spatial frequency feature from the source images to guide its fusion. For the directional detail subbands, we introduce the convolutional sparse representation, which is a model that can achieve sparse representation of an entire subband, to represent each subband so as to obtain the activity level measurement to fuse directional detail subbands. Finally, the fused image can be obtained by the inverse MSNLGA of the fused subbands. The experimental results show that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both visual and quantitative evaluations. (C) 2019 Elsevier B.V. All rights reserved.