Camouflaged Object Detection (COD) is a challenging visual task due to its complex contour, diverse scales, and high similarity to the background. Existing COD methods encounter two predicaments: One is that they are prone to falling into local perception, resulting in inaccurate object localization; Another issue is the difficulty in achieving precise object segmentation due to a lack of detailed information. In addition, most COD methods typically require larger parameter amounts and higher computational complexity in pursuit of better performance. To this end, we propose a global localization perception and local guidance refinement network (PRNet), that simultaneously addresses performance and computational costs. Through effective aggregation and use of semantic and details information, the PRNet can achieve accurate localization and refined segmentation of camouflaged objects. Specifically, with the help of a Cascaded Attention Perceptron (CAP) designed, we can effectively integrate and perceive multi-scale information to localize camouflaged objects. We also design a Guided Refinement Decoder (GRD) in a top-down manner to extract context information and aggregate details to further refine camouflaged prediction results. Extensive experimental results demonstrate that our PRNet outperforms 12 state-of-the-art models on 4 challenging datasets. Meanwhile, the PRNet has a smaller number of parameters (12.74M), lower computational complexity (10.24G), and real-time inference speed (105FPS). Source codes are available at https://github.com/hu-xh/PRNet.

Multi-focus image fusion (MFIF) attempts to achieve an “all-focused” image from multiple source images with the same scene but different focused objects. Given the lack of multi-focus image sets for network training, we propose a self-supervised residual feature learning model in this paper. The model consists of a feature extraction network and a fusion module. We select image super-resolution as a pretext task in the MFIF field, which is supported by a new residual gradient prior discovered by our theoretical study for low- and high-resolution (LR-HR) image pairs, as well as for multi-focus images. In the pretext task, our network’s training set is LR-HR image pairs generated from natural images, and HR images can be regarded as pseudo-labels of LR images. In the fusion task, the trained network extracts residual features of multi-focus images firstly. Secondly, the fusion module, consisting of an activity level measurement and a new boundary refinement method, is leveraged for the features to generated decision maps. Experimental results, both subjective evaluations and objective evaluations, demonstrate that our approach outperforms other state-of-the-art fusion algorithms.

Studies on the traditional support vector machine (SVM) implicitly assume that the costs of different types of mistakes are the same and minimize the error rate. On the one hand, it is not enough for many practical applications to rely solely on the error rate, which reflects only the average classification ability of a classifier. It is also of great significance to consider the performance of classifiers from the perspective of each sample. On the other hand, many real-world problems, such as credit card fraud detection, intrusion detection, oil-spill detection and cancer diagnosis, usually involve substantially unequal misclassification costs. To solve this problem, many works on the cost-sensitive SVM (CS-SVM) have emerged. The misclassification costs for this model are generally provided by domain experts. Inspired by the concept of the CS-SVM, we propose a new SVM with sample-based misclassification cost invariance with the aim of constructing a relatively reliable classifier. The relatively reliable classifier is defined as the one with low probabilities of finding a classifier that correctly classifies each misclassified sample. Note that the cost is determined by the inherent characteristics of each sample rather than being subjectively assigned, so we denote the proposed classifier as the objective-cost-sensitive SVM (OCS-SVM). The experimental results demonstrate the superiority of the proposed method compared with nine other commonly used classifiers.