Abstract.A new method of combined multi-scale decomposition and improved sparse representation (MSD-ISR) used in image fusion is proposed in this paper which has three advantages. As is known that it is hard to determine the decomposition level and it may achieve low contrast in multi-scale decomposition. The proposed MSD-ISR method can solve this problem and on the other hand, we get a higher efficiency in the process of sparse decomposition as well as better preserve the information of the source image. Experimental results show that the proposed MSD-ISR method can achieve better fusion results.

Abstract. In this paper, a multi-focus image fusion algorithm based on motivated pulse coupled neural networks using nonsubsampled contourlet transform (NSCT) is proposed. Firstly, NSCT is used to decompose the source image in multi scale and multi directions to obtain the low frequency coefficients and high frequency coefficients. Secondly, Fourier transform method is used for saliency detection. We choose the coefficients which have larger saliency for high frequency coefficients. In the process of choosing the low frequency, the spatial frequency is used as the input of the Pulse coupled neural network (PCNN) to obtain the firing times of the low frequency. If the firing times of two images are not the same, then we choose coefficients which have larger firing times. Otherwise, coefficients which have larger saliency will be chosen. Lastly, inverse transformation of NSCT is used to obtain the fused image. The proposed method is compared with four other common methods, and the experimental results show that the proposed method performs better on both subjective and objective evaluation indicators.