Aiming at problem of loss of image detail and edge information in image fusion caused by conventional fusion method

a novel image fusion method via L0-regularized total variation is proposed. An average filtering is performed on source images to extract edge feature

and the weights of source images are calculated by gradient of feature map. Based on target image

and acts as the fidelity term which penalizes the departure from the initial image. A target gradient map is also made to regularize the gradient of fused image. By introducing auxiliary variables

an alternating minimization algorithm is used to get the fused image. Experiments are conducted by multi-focus images as well as infrared and visible images

results of fused images are computed by some quality metrics. Comparing with discrete cosine harmonic wavelet transform fusion method

average gradient is increased by0. 71 % and 15. 71 %

and standard deviation is increased by 4. 97 % and 4. 77 %

as well as QUV/Fis increased by 0. 94 % and 6. 13 %

respectively

the LUV/Fis decreased by 5. 47 % and 17. 37 %. It concludes that the proposed algorithm obtains remarkable results both in visual perception and objective metrics.