Fusion methods based on dynamic-segmented morphological wavelet or cut and paste for multifocus images

This paper presents a dynamic-segmented morphological wavelet fusion method (DSMWF) and a dynamic-segmented cut and paste fusion method (DSCP). Non-focus regions tend to spread around within multifocus images. The proposed methods firstly divide each multifocus image into segments and select each sharpest segment at the same location within all images as the “focus segment”, based on DCT spectrum concentration on high-frequency sub-band. Each focus segment is further divided into smaller blocks having uniform visual complexity d based on Laplacian edge density. Finally, method DSMWF applies a single-level variable size morphological wavelet fusion method to each block of 2d×2d and method DSCP applies direct cut and paste of the sharpest block to each block of 2d×2d, respectively, to obtain a fused image. The experimental results demonstrate that (a) the PSNR of the fused image using DSMWF is 2–3 dB better than that of MMWF in an average, (b) the occurrence of reconstructing both pixels with position error and underflow value is greatly reduced with DSMWF, (c) the performance of DSCP is much superior to that of both MMWF and DSMWF, and (d) block sharpness assessment based on DCT spectrum concentration on high frequency sub-band performs better than DWT and Laplacian edge for this application.