A novel image fusion algorithm based on nonsubsampled contourlet transform (NSCT) and spiking cortical model (SCM) is proposed in this paper, aiming at solving the fusion problem of multifocus images. The fusion rules of subband coefficients of NSCT are discussed, and a new maximum selection rule (MSR) is defined to fuse low frequency coefficients instead of using traditional MSR directly. For the fusion rule of high frequency coefficients, spatial frequency (SF) of each high frequency subband is considered as the gradient features of images to motivate SCM networks and generate pulse of neurons, and then the time matrix of SCM is set as criteria to select coefficients of high frequency subband. Experimental results and visual evaluation demonstrate the effectiveness of the proposed fusion method. Objective tests and analysis conducted under different noised source image environments proved the robustness of the proposed fusion method.
Content-based image retrieval (CBIR) of images, especially those with different orientation, scale changes and noise affects, are a challenging and important problem in the image analysis. This paper proposes an effective scheme for rotation and scale invariant antinoise retrieval using pulse-coupled neural network (PCNN) features. The PCNN generates series of pulse images, which are binary and represent different features of the original image. The series of pulse images can be then calculated to an entropy sequence called the feature of the image. The experimental results show that the retrieval scheme is effective in extracting rotation and scale invariant features and it also performs better robustness to noise.