Image smoothing is a fundamental technique in image processing, designed to eliminate perturbations and textures while preserving dominant structures. It plays a pivotal role in numerous high-level computer vision tasks. More recently, both traditional and deep learning-based smoothing methods have been developed. However, existing algorithms frequently encounter issues such as gradient reversals and halo artifacts. Furthermore, the smoothing strength of deep learning-based models, once trained, cannot be adjusted for adapting different complexity levels of textures. These limitations stem from the inability of previous approaches to achieve an optimal balance between smoothing intensity and edge preservation. Consequently, image smoothing while maintaining edge integrity remains a significant challenge. To address these challenges, we propose a novel edge-aware smoothing model that leverages a relative wavelet domain representation. Specifically, by employing wavelet transformation, we introduce a new measure, termed Relative Wavelet Domain Representation (RWDR), which effectively distinguishes between textures and structures. Additionally, we present an innovative edge-aware scale map that is incorporated into the adaptive bilateral filter, facilitating mutual guidance in the smoothing process. This paper provides complete theoretical derivations for solving the proposed non-convex optimization model. Extensive experiments substantiate that our method has a competitive superiority with previous algorithms in edge-preserving and artifact removal. Visual and numerical comparisons further validate the effectiveness and efficiency of our approach in several applications of image smoothing.

The goal of few-shot segmentation (FSS) is to segment unlabeled images belonging to previously unseen classes using only a limited number of labeled images. The main objective is to transfer label information effectively from support images to query images. In this study, we introduce a novel meta-learning framework called layer-wise mutual information (LayerMI), which enhances the propagation of label information by maximizing the mutual information (MI) between support and query features at each layer. Our approach involves the utilization of a LayerMI Block based on information-theoretic co-clustering. This block performs online co-clustering on the joint probability distribution obtained from each layer, generating a target-specific attention map. The LayerMI Block can be seamlessly integrated into the meta-learning framework and applied to all convolutional neural network (CNN) layers without altering the training objectives. Notably, the LayerMI Block not only maximizes MI between support and query features but also facilitates internal clustering within the image. Extensive experiments demonstrate that LayerMI significantly enhances the performance of baseline and achieves competitive performance compared to state-of-the-art methods on three challenging benchmarks: PASCAL- $5^{i}$ , COCO- $20^{i}$ , and FSS-1000.

Image smoothing aims to remove insignificant textures or perturbations while maintaining meaningful structures, which is a fundamental technique widely used in the vision and graphics fields. However, the performance of deep methods is often limited by ground-truth images and edge preservation ability in this task. To address the two issues, we proposed self-supervised dual generative networks (S2DGNet) with the Cauchy regularized variational model. The dual networks integrate information on textures and structures under the alternating direction method of multipliers (ADMM) framework, improving the model’s edge-keeping. In addition, we created a new smoothing dataset named the ECS dataset. Experiment results demonstrate the noticeable performance improvement of the S2DGNet over the existing solutions.

In this work, we revisit the prior mask guidance proposed in “Prior Guided Feature Enrichment Network for Few-Shot Segmentation”. The prior mask serves as an indicator that highlights the region of interests of unseen categories, and it is effective in achieving better performance on different frameworks of recent studies. However, the current method directly takes the maximum element-to-element correspondence between the query and support features to indicate the probability of belonging to the target class, thus the broader contextual information is seldom exploited during the prior mask generation. To address this issue, first, we propose the Context-aware Prior Mask (CAPM) that leverages additional nearby semantic cues for better locating the objects in query images. Second, since the maximum correlation value is vulnerable to noisy features, we take one step further by incorporating a lightweight Noise Suppression Module (NSM) to screen out the unnecessary responses, yielding high-quality masks for providing the prior knowledge. Both two contributions are experimentally shown to have substantial practical merit, and the new model named PFENet++ significantly outperforms the baseline PFENet as well as all other competitors on three challenging benchmarks PASCAL-5$^{i}$, COCO-20$^{i}$and FSS-1000. The new state-of-the-art performance is achieved without compromising the efficiency, manifesting the potential for being a new strong baseline in few-shot semantic segmentation.

Deep learning has been extensively applied to hyperspectral image (HSI) classification and has achieved significant success. However, the number of labeled samples available for HSI classification tasks is typically limited in practical applications, which makes the high-accuracy of HSI small-sample classification still a challenging research task. Therefore, metric-based prototypical networks for few-shot learning (FSL) have become increasingly popular. However, the majority of existing FSL methods typically have problems with biased prototypes and domain shifts. To address these issues, this article proposed a prototype rectification network framework for cross-domain few-shot HSI classification. Specifically, to obtain more representative prototypes, we designed a query-guided prototype rectification module, which can rectify the feature distribution of the support set prototype and obtain a more representative prototype for subsequent training tasks. Then, we introduced a prototype-based interclass loss function to alleviate the interclass confusion that may result from prototype rectification. Furthermore, we construct an intermediate domain between the source domain and the target domain to alleviate domain shift, which helps mitigate the difficulties of domain transfer and achieve a more comprehensive domain alignment. The experimental results on four publicly available HSI datasets demonstrate that our proposed method outperforms the existing FSL methods.

In this work, we address the challenging issue of few-shot segmentation. Existing methods mainly explore the target object through the semantic similarity between the query and support pixels. However, the semantic similarity often fails to deal well with the target objects with large variations in appearance and the error predictions along the boundary. To this end, we propose a novel spatial similarity guidance network (S2GNet), which adaptively integrates spatial information with semantic information for building a target-aware correlation region to enhance the target object localization. To promote the overall spatial position understanding of the target object, we exploit boundaries as crucial guidance for spatial information. Thus we jointly train a boundary detection task and a segmentation task in an end-to-end way. With that, a target-aware attention module is further proposed to capture the target correlation region by combining the spatial similarity with the semantic similarity for each pair of pixels in the query image, which refines the location of the target object effectively and improves the segmentation performance. Extensive experiments on both PASCAL-5iand COCO-20idatasets show that our approach can achieve state-of-the-art performances.

In this paper, we address the problem of fusing multi-focus images in same scenes. We propose a gradient guidance progressive network for multi-focus image fusion. We explicitly extract gradient features of images, and introduce the gradient guidance progressive module to integrate effectively features. In the module, we employ low-resolution features with large receptive fields to detect focused areas far away from boundaries. While for high-resolution features incorporating detailed gradient features, we only focus on optimizing outputs near boundaries. Benefiting from the separate operations on both areas far away from and near boundaries, the proposed method makes accurate focus region detection with detailed boundaries. Experimental results demonstrate the effectiveness and superiority of the proposed method compared with the state-of-the-art methods.

Few-shot segmentation has got a lot of concerns recently. Existing methods mainly locate and recognize the target object based on a cross-guided way that applies masked target object features of support(query) images to make a feature matching with query(support) images. However, there are some differences between support images and query images because of large appearance and scale variation, which will lead to inaccurate and incomplete segmentation. This problem inspired us to explore the local coherence of the image to guide the segmentation. We try to get some target pixels in the query image and apply these pixels to search for more target pixels in the query image. In this work, we propose a novel network that combines a universal cross-guided branch with a new pseudo-interacting guided branch. Specifically, we first employ the universal cross-guided branch to produce a pseudo-labeling that represents the probability of each pixel belonging to the target object. Then we design a pseudo-interacting guided branch, which applies some pixels with high probabilities based on generated pseudo-labeling to segment the target object in the query image and revises the results of the cross-guided branch simultaneously. Extensive experiments show that our approach outperforms state-of-the-art methods on both PASCAL-5iand COCO-20idatasets.

Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with a few annotations. Previous methods mainly establish the correspondence between support images and query images with global information. However, human perception does not tend to learn a whole representation in its entirety at once. In this paper, we propose a novel network to build the correspondence from subparts, parts and whole. Our network mainly contain two novel designs: we firstly adopt graph convolutional network to make pixels not only contain the information of each pixel itself but also include its contextual pixels, and then a learnable Graph Affinity Module(GAM) is proposed to mine more accurate relationships as well as common object location inference between the support images and the query images. Experiments on the PASCAL-5idataset show that our method achieves state-of-the-art performance.

State-of-the-art semantic segmentation algorithms are based on deep convolutional neural networks, which aim to exploit a great quantity of labeled samples to predict the class label for each pixel in an image. Few-shot segmentation alleviate the data labeling task, learning a model given only a few labeled samples, which adapts well to unseen classes. Previous works only extract spatial pixels relationship to build the guidance, which ignores the important feature channel information that contain abundant local semantic structure in the images. In this paper, we propose a novel channel interaction network (CINet) for few-shot semantic segmentation. It mainly consists of three novel steps: a local enhancement module that aggregates the spatial pixels connection in both query images and support images; a channel interaction module that captures the similar channel from relationship between each pair of images for segmentation guidance; and a residual attention module that exploiting residual connection of feature scales to alleviate spatial inconsistency between query images and corresponding example images. Extensive experiments on PASCAL-5idataset demonstrate that our model outperforms state-of-the-art methods without parameters increase sharply.

Few-shot segmentation refers to learn a segmentation model that can be generalized to novel classes with limited labeled images. Establishing the correspondence between support images and query images effectively has a considerable effect on guiding the segmentation of query images. Most existing methods mainly adopt a trained classification network as the backbone, nevertheless, the classification tasks only focus on the most discriminate regions of the target rather than the targets' integrity and the most discriminate regions may not be part of the target we need to segment while multiple classes object included in images. Besides, there exists another question that the most discriminate regions of the target in support image also do not necessarily appear in query images because of occlusion or incomplete object. All these may cause the correspondence between two images inaccurately. To tackle these problems, we propose a Target-aware Network(TaNet). Our network has two objectives: (1) increasing both intra-object similarity and inter-object dissimilarity for query image and support image to make each object more complete rather than highlight the most discriminate regions; (2) adaptively generating target-aware correspondence between support images and query images. Experiments on PASCAL-5iand COCO-20ishow that our method achieves state-of-the-art performance.