Infrared and multi-type images fusion algorithm based on contrast pyramid transform

• Laplacian sharpening is introduced to enhance the image contrast.
• Otsu method combined with improved weighted fusion is employed to fuse the low-frequency images.
• Top-bottom hats transform is utilized to highlight the bright and dark details of objects and preserve the salient features.
• The second fusion process further makes the thermal targets notable and edges legible.
• Four groups of multi-type images are tested and both qualitative and quantitative evaluations are given.

A fusion algorithm for infrared and multi-type images based on contrast pyramid transform (CPT) combined with Otsu method and morphology is proposed in this paper. Firstly, two sharpened images are combined to the first fused image based on information entropy weighted scheme. Afterwards, two enhanced images and the first fused one are decomposed into a series of images with different dimensions and spatial frequencies. To the low-frequency layer, the Otsu method is applied to calculate the optimal segmentation threshold of the first fused image, which is subsequently used to determine the pixel values in top layer fused image. With respect to the high-frequency layers, the top-bottom hats morphological transform is employed to each layer before maximum selection criterion. Finally, the series of decomposed images are reconstructed and then superposed with the enhanced image processed by morphological gradient operation as a second fusion to get the final fusion image. Infrared and visible images fusion, infrared and low-light-level (LLL) images fusion, infrared intensity and infrared polarization images fusion, and multi-focus images fusion are discussed in this paper. Both experimental results and objective metrics demonstrate the effectiveness and superiority of the proposed algorithm over the conventional ones used to compare.