The edge detection plays an important role in post-processing of PolSAR images. It is still a great challenge for extracting all the edge features and suppress speckle noises, especially when weak/strong edges appear simultaneously outside and within heterogenous areas. In this paper, a novel hybrid edge detection framework is proposed to address this problem. The proposed method is designed by fusing two initial edge detectors, which can detect complementary edge information. One is an improved polarimetric constant false alarm rate (IP_CFAR) edge detector, which can detect weak edges well, but fail to detect the edges in the heterogeneous regions. The other is the proposed weighted gradient-based (WG) detector which can detect edges in heterogeneous areas well, but loses some weak edges and produces some false edges due to the speckle noises. Secondly, based on the two detectors above, a wavelet-based hybrid edge detection method is proposed by combining their merits and suppress their shortcomings. To fuse them effectively, wavelet transformation is utilized and semantic rules are defined to extract their advantages. Moreover, a despeckling scheme is designed to suppress the false edges in the wavelet domain. Experimental results demonstrate that the proposed method outperforms the state-of-art methods in extracting both weak edges and strong edges within heterogeneous regions.

Infrared and visible image fusion is currently an important research direction in the field of multimodal image fusion, which aims to utilize the complementary information between infrared images and visible images to generate a new image containing richer information. In recent years, many deep learning-based methods for infrared and visible image fusion have emerged. However, most of these approaches ignore the importance of semantic information in image fusion, resulting in the generation of fused images that do not perform well enough in human visual perception and advanced visual tasks. To address this problem, we propose a semantic prior knowledge-driven infrared and visible image fusion method. The method utilizes a pre-trained semantic segmentation model to acquire semantic information of infrared and visible images, and drives the fusion process of infrared and visible images through semantic feature perception module and semantic feature embedding module. Meanwhile, we divide the fused image into each category block and consider them as components, and utilize the regional semantic adversarial loss to enhance the adversarial network generation ability in different regions, thus improving the quality of the fused image. Through extensive experiments on widely used datasets, the results show that our approach outperforms current leading algorithms in both human eye visualization and advanced visual tasks. The source code is available at https://github.com/tianzhiya/SPDFusion.

As an interesting technique that allows data extraction and image recovery without any loss, reversible data hiding in encrypted images is an area of great concern in the field of information security. In this paper, a new reversible data hiding method with high embedding capacity is proposed based on hybrid prediction and Huffman coding. The combination of two embedding mechanisms is innovatively designed to improve the embedding capacity according to different parts of the original image, i.e., the most significant bit-plane and the remaining seven bit-planes. In the first part of this method, the prediction value of each pixel is obtained by calculating the average value of its two neighboring pixels, and all of the most significant bits can be vacated to accommodate additional data. In the second part, the prediction value of each pixel is calculated using the median edge detector predictor, on which the tag map is built. Then, Huffman coding is used to compress the tag map so that a large amount of vacant space is obtained. Finally, the secret data can be embedded into the vacated space by directly using bit substitution. Compared with some recently reported methods, experimental results and analysis have demonstrated that an original image with high visual symmetry/quality can be recovered. Also, larger embedding capacity can be achieved, such as 3.3894 bpp and 3.2824 bpp, for BOSSBase and BOWS2 databases, respectively.