A NSST-based infrared and visible image fusion method focusing on luminance effect

Abstract

Generally, the fused image shows fully the actual situation of the scene and contains more detailed information. However, most fusion methods miss the details of the fusion image and confuses the contrast of the raw scene. To solve this problem, we propose a fusion algorithm based on non-subsampled shearlet transform (NSST) that particularly pays attention to the influence of light intensity when calculating the fusion coefficient. The method first decomposes the input images into high- and low-frequency coefficients through NSST. Then regarding the high-frequency coefficients, we calculate the phase consistency (PC) of the decomposed images, and the results are combined with the adaptive simplified pulse coupled neural network (SPCNN) to compose parameter. Meanwhile, for the low-frequency coefficient, the optimal brightness entropy (OBE) of the input images is obtained as the fusion basis. The next step is to fuse the high- and low-frequency sub-band coefficients by the designed fusion rule, and obtain final image through NSST inverse transformation. Experiments show that our method not only keeps well the image details and maintains the overall image luminance while taking care of the overall effect of the image, but also gets a leading position in some evaluation indicators.