Segmenting polyps from colonoscopy images is very important in clinical practice since it provides valuable information for colorectal cancer. However, polyp segmentation remains a challenging task as polyps have camouflage properties and vary greatly in size. Although many polyp segmentation methods have been recently proposed and produced remarkable results, most of them cannot yield stable results due to the lack of features with distinguishing properties and those with high-level semantic details. Therefore, we proposed a novel polyp segmentation framework called contrastive Transformer network (CTNet), with three key components of contrastive Transformer backbone, self-multiscale interaction module (SMIM), and collection information module (CIM), which has excellent learning and generalization abilities. The long-range dependence and highly structured feature map space obtained by CTNet through contrastive Transformer can effectively localize polyps with camouflage properties. CTNet benefits from the multiscale information and high-resolution feature maps with high-level semantic obtained by SMIM and CIM, respectively, and thus can obtain accurate segmentation results for polyps of different sizes. Without bells and whistles, CTNet yields significant gains of 2.3%, 3.7%, 3.7%, 18.2%, and 10.1% over classical method PraNet on Kvasir-SEG, CVC-ClinicDB, Endoscene, ETIS-LaribPolypDB, and CVC-ColonDB respectively. In addition, CTNet has advantages in camouflaged object detection and defect detection. The code is available at https://github.com/Fhujinwu/CTNet .

The convolutional neural network (CNN)-based multi-focus image fusion methods which learn the focus map from the source images have greatly enhanced fusion performance compared with the traditional methods. However, these methods have not yet reached a satisfactory fusion result, since the convolution operation pays too much attention on the local region and generating the focus map as a local classification (classify each pixel into focus or de-focus classes) problem. In this paper, a global-feature encoding U-Net (GEU-Net) is proposed for multi-focus image fusion. In the proposed GEU-Net, the U-Net network is employed for treating the generation of focus map as a global two-class segmentation task, which segments the focused and defocused regions from a global view. For improving the global feature encoding capabilities of U-Net, the global feature pyramid extraction module (GFPE) and global attention connection upsample module (GACU) are introduced to effectively extract and utilize the global semantic and edge information. The perceptual loss is added to the loss function, and a large-scale dataset is constructed for boosting the performance of GEU-Net. Experimental results show that the proposed GEU-Net can achieve superior fusion performance than some state-of-the-art methods in both human visual quality, objective assessment and network complexity.

The Local Binary Pattern (LBP) and its variants have shown the effectiveness in texture images classification, face recognition and other applications. However, most of these LBP methods only focus on the histogram of LBP patterns and ignore the spatial contextual information between LBP patterns. In this paper, we propose a two dimensional Local Binary Pattern (2DLBP) method, which use a sliding window to count the weighted occurrence number of the rotation invariant uniform LBP pattern pairs to obtain the spatial contextual information. The multiresolution 2D-LBP features can also be obtained when the radius of 2D-LBP is changed. At last, a two-stages classifier which acts as an ensemble learning step is followed to achieve an accurate classification by combining the predictions on each 2D-LBP with single resolution. Theoretical proof shows that the proposed 2DLBP is a general framework and can be integrated on other LBP variants to derive new feature extraction methods. Experimental results show that, the proposed method achieves 99.71%, 97.09%, 98.48% and 49.00% classification accuracy on the public “Brodatz”, “CUReT”, “UIUC” and “FMD” texture image databases. Compared with the original LBP and its variants, the proposed method obtains higher classification accuracy under different cases, and simultaneously owns shorter time complexity.

In this paper, a Hessian matrix based multi-focus image fusion method is proposed. First, the integral map is introduced for fast compute the Hessian matrix of source images at different scales, and the multi-scale Hessian matrix of source image is obtained. Second, the multi-scale Hessian matrix is used to decompose each source image into two kinds of regions: the feature and background regions. In order to improve the fusion performance, two new focus measures based on the multi-scale Hessian matrix and two different fusion strategies for both feature and background regions are utilized to obtain the initial decision maps, respectively. Finally, the final decision map for image fusion is achieved by post-processing on the results of the previous step. The proposed method is a primary attempt to introduce image feature and background regions decomposition strategies in the field of multi-focus image fusion. The experimental results also show that our method outperforms the existing image fusion methods in both visual perception and objective evaluations.