Fractal dimension based parameter adaptive dual channel PCNN for multi-focus image fusion

Abstract

Multi-focus image fusion combines the focused parts of multiple images of the same scene to generate a fully focused image. Dual Channel Pulse coupled neural network (DCPCNN) is frequently used in image fusion framework due to its characteristics like global coupling, pulse synchronization of neurons and simultaneous processing of two images. However, it suffers from the manual setting of its parameters. This paper proposes a transform domain multi-focus image fusion method based on a novel parameter adaptive DCPCNN (PA-DCPCNN) model, in which the parameters are adaptively estimated using the inputs. Moreover, the linking strengths of the PA-DCPCNN model are computed per neuron using a new fractal dimension based focus measure (FDFM). First, the multi-scale and multi-direction decomposition of source images are performed using non-subsampled contourlet transform (NSCT) to obtain the low-pass sub-band and a number of band-pass directional sub-bands. Second, the band-pass directional sub-bands of the source images are fused using the PA-DCPCNN model. Third, the low-pass sub-bands of source images are combined using multi-scale morphological gradient (MSMG) and FDFM to generate the fused low-pass sub-band. Finally, the fused image is obtained by applying inverse NSCT to the fused sub-bands. The performance of the proposed method is compared with fourteen state-of-the-art multi-focus image fusion methods using six objective quality metrics. Experimental results demonstrate that the proposed method is competitive with the state-of-the-art methods in terms of both subjective and objective assessments.