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In recent years, image fusion has been a research hotspot. However, it is still a big challenge to balance the problems of noiseless image fusion and noisy image fusion. In order to improve the weak performance and low robustness of existing image fusion algorithms in noisy images, an infrared and visible image fusion algorithm based on optimized low-rank matrix factorization with guided filtering is proposed. First, the minimized error reconstruction factorization is introduced into the low-rank matrix, which effectively enhances the optimization performance, and obtains the base image with good filtering performance. Then using the base image as the guide image, the source image is decomposed into the high-frequency layer containing detail information and noise, and the low-frequency layer containing energy information through guided filtering. According to the noise intensity, the sparse reconstruction error is adaptively obtained to fuse the high-frequency layers, and the weighted average strategy is utilized to fuse the low-frequency layers. Finally, the fusion image is obtained by reconstructing the pre-fused high-frequency layer and the pre-fused low-frequency layer. The comparative experiments show that the proposed algorithm not only has good performance for noise-free images, but more importantly, it can effectively deal with the fusion of noisy images.

Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms.

Fish venom remains a virtually untapped resource. There are so few fish toxin sequences for reference, which increases the difficulty to study toxins from venomous fish and to develop efficient and fast methods to dig out toxin genes or proteins. Here, we utilized Chinese yellow catfish (Pelteobagrus fulvidraco) as our research object, since it is a representative species in Siluriformes with its venom glands embedded in the pectoral and dorsal fins. In this study, we set up an in-house toxin database and a novel toxin-discovering protocol to dig out precise toxin genes by combination of transcriptomic and proteomic sequencing. Finally, we obtained 15 putative toxin proteins distributed in five groups, namely Veficolin, Ink toxin, Adamalysin, Za2G and CRISP toxin. It seems that we have developed a novel bioinformatics method, through which we could identify toxin proteins with high confidence. Meanwhile, these toxins can also be useful for comparative studies in other fish and development of potential drugs.