Multi-focus image fusion is the process of fusing images of the same scene with different focus ranges. However, pixel misclassification and artifacts caused by noise have been unavoidable problems in fusion methods. To overcome this problem, we propose gradient tensor high-order singular value decomposition (HOSVD) to fuse multi-focus images. The eight-direction Sobel gradient operator is used to obtain second-order gradients in different directions. Tensor-based information processing can effectively represent high-dimensional information and extract structural information from multi-focus images. Therefore, second-order gradient matrices of different images are formed into tensors. The gradient tensor of the images is transformed by HOSVD to obtain the core tensor and factor matrixes. The images are better represented by multiplying the core tensor with the factor matrix corresponding to the direction of the image. In the core tensor, the larger singular values represent basic information and the small singular values high-frequency information. The noise generally belongs to high-frequency information, so we select the larger singular values in the core tensor as activity level variables of the image to reduce noise. Experimental results demonstrate that the proposed fusion method can effectively reduce noise effects and achieve state-of-the-art fusion performance in both objective metrics and visual quality.
Image colorization is a technology to transform gray-scale image into natural color image, which is always a challenging task in image processing. In this paper, we design a deep neural network model including multi-scale convolutional down-sampling and attention mechanism, which can effectively solve the problem of coloring gray remote sensing image. First, we input the grayscale remote sensing image into the feature extractor of our model for feature extraction and multi-scale down-sampling. Second, the feature map extracted by the feature extractor is input into the squeeze-andexcitation attention mechanism module to enhance the extraction of key information. Finally, the feature map output by the feature extractor and squeeze-and-excitation module is input into the feature reconstructor for feature reconstruction and color restoration, and then color remote sensing images are obtained. Unlike the conventional methods, our method is an end-to-end model, and without any human interaction in the coloring process. The experimental results show that the color remote sensing image generated by our method is superior to the existing methods in both subjective and objective metrics.
Using the simplified pulse coupled neural network (S-PCNN) model and hue, saturation and value (HSV) color space, an effective color image fusion algorithm was proposed in this paper. In the HSV color space, using S-PCNN, the feature region clustering of each component (H, S, V) was done; the fusion of the various components of the different source images based on the oscillation frequency graph (OFG) was achieved; then through the inverse HSV transform to get RGB color image, the fusion of the color image were realized. Experimental results show that the algorithm both in the subjective visual effect and objective evaluation criteria is superior to other common color image fusion algorithms.
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