Combine Target Extraction and Enhancement Methods to Fuse Infrared and LLL Images

For getting the useful object information from infrared image and mining more detail of low light level (LLL) image, we propose a new fusion method based on segmentation and enhancement methods in the paper. First, using 2D maximum entropy method to segment the original infrared image for extracting infrared target, enhancing original LLL image by Zadeh transform for mining more detail information, on the basis of the segmented map to fuse the enhanced LLL image and original infrared image. Then, original infrared image, the enhanced LLL image and the first fused image are used to realize fusion in non-subsampled contourlet transform (NSCT) domain, we get the second fused image. By contrast of experiments, the fused image of the second fused method’s visual effect is better than other methods’ from the literature. Finally, Objective evaluation is used to evaluate the fused images’ quality, its results also show that the proposed method can pop target information, improve fused image’s resolution and contrast

MDLatLRR: A novel decomposition method for infrared and visible image fusion

Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent low-rank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We develop a novel image fusion framework based on MDLatLRR, which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts, and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.Comment: IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 table

Image Sequence Fusion and Denoising Based on 3D Shearlet Transform

We propose a novel algorithm for image sequence fusion and denoising simultaneously in 3D shearlet transform domain. In general, the most existing image fusion methods only consider combining the important information of source images and do not deal with the artifacts. If source images contain noises, the noises may be also transferred into the fusion image together with useful pixels. In 3D shearlet transform domain, we propose that the recursive filter is first performed on the high-pass subbands to obtain the denoised high-pass coefficients. The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low-pass subband is fused to use the fusion rule of the weighted sum. Experimental results demonstrate that the proposed algorithm yields the encouraging effects