The purpose of infrared and visible image fusion is to generate images with prominent targets and rich information which provides the basis for target detection and recognition. Among the existing image fusion methods, the traditional method is easy to produce artifacts, and the information of the visible target and texture details are not fully preserved, especially for the image fusion under dark scenes and smoke conditions. Therefore, an infrared and visible image fusion method is proposed based on visual saliency image and image contrast enhancement processing. Aiming at the problem that low image contrast brings difficulty to fusion, an improved gamma correction and local mean method is used to enhance the input image contrast. To suppress artifacts that are prone to occur in the process of image fusion, a differential rolling guidance filter (DRGF) method is adopted to decompose the input image into the basic layer and the detail layer. Compared with the traditional multi-scale decomposition method, this method can retain specific edge information and reduce the occurrence of artifacts. In order to solve the problem that the salient object of the fused image is not prominent and the texture detail information is not fully preserved, the salient map extraction method is used to extract the infrared image salient map to guide the fusion image target weight, and on the other hand, it is used to control the fusion weight of the basic layer to improve the shortcomings of the traditional ‘average’ fusion method to weaken the contrast information. In addition, a method based on pixel intensity and gradient is proposed to fuse the detail layer and retain the edge and detail information to the greatest extent. Experimental results show that the proposed method is superior to other fusion algorithms in both subjective and objective aspects.

Hyperspectral image (HSI) classification with limited training samples remains a challenging task due to the high cost and time consumption of collecting labeled samples. In recent years, semi-supervised image classification methods have garnered widespread attention, as they address the scarcity of labeled samples and enhance classification accuracy by leveraging the inherent relationships between labeled and unlabeled samples. In this paper, we introduce a semi-supervised classification method based on multi-scale spectral-spatial graph attention network (MSSGAT). We construct multiple neighborhood graphs with different scales, where all samples (including training and testing samples) are represented as nodes, and exploit the spectral-spatial features with different receptive fields. These features are then fed into the graph attention network to learn attention coefficients between neighboring nodes and self-attention coefficients for target nodes, which are further aggregated to extract more discriminative features for enhancing HSI classification accuracy. The experimental results on the Indian Pines, Salinas and Pavia University datasets demonstrate its competitive performance. The overall accuracy ( OA ) scores are 99.36 %, 99.59 % and 96.85 %, respectively, which exceed the state-of-the-art ( SOTA ) models.

The most important factor affecting the quality of image is the brightness and contrast. In order to improve the brightness and contrast of the image, this paper presents an adaptive image enhancement method. This method sets a threshold value according to the grayscale range of the image. If it is smaller than the threshold value, this paper uses auto gray levels enhancement. While if it is greater than the threshold value, this paper uses weighted gray levels equalization enhancement. Adaptive image enhancement method can not only enhance the low-contrast images, but also can enhance the dark images or the bright images. This method has a good robustness and the enhanced image has a better visual effect.