Image super-resolution aims to enhance the detail representation and visual clarity of low-resolution images. Although latent diffusion model (LDM)-based super-resolution approaches have achieved remarkable progress, their use of aggressive downsampling to compress degraded images into the latent space often results in loss of high-frequency details, thereby limiting the structural fidelity of the reconstructed images. To address this, we propose a plug-and-play Edge Reconstruction Network (ERN) that recovers high-resolution edge prior from low-resolution inputs. By explicitly including this high-resolution edge prior as structural priors in the diffusion process, the model’s ability to reconstruct high-frequency details can be greatly improved. In ERN, new edges of images are calculated using edge pixels. Since edge pixels are treated as discrete sampling points on the edge curve, the neural network interpolates edge pixels only, effectively eliminating non-edge information interference, thereby reducing jagged edges and block artifacts. In addition, the network’s reconstruction accuracy and robustness in complex structure scenes improve significantly as it learns the mapping between low-resolution images and their corresponding high-resolution edge images. Another key contribution is an automatic label generation method based on surface fitting, which extracts edge labels with quadratic polynomial accuracy from GT images, providing reliable supervision for edge reconstruction and alleviating the scarcity of high-quality edge labels. Extensive experiments demonstrate ERN’s effectiveness on SR task. It can be seamlessly integrated into existing LDM-based methods, with only ∼ 15M additional parameters yielding a 0.1 dB ∼ 1.2 dB PSNR gain, achieving a favorable balance between performance and computational cost. The code will be released at https://github.com/YunyangXu/ERN .

Semi-supervised learning (SSL) has emerged as a promising paradigm for medical image segmentation, aiming to alleviate the scarcity of high-quality annotations by combining limited labeled and abundant unlabeled data. However, existing SSL methods suffer from inherent limitations: 1) consistency regularization overly relies on enforcing prediction consistency under different perturbations, neglecting deep exploration of semantic and discriminative features; 2) pseudo-labeling methods are prone to introducing noise, which in turn undermines the stability of model training. To enable high-quality and more stable model learning, we propose a common pattern prior-driven network (CPP-Net) for semi-supervised medical image segmentation. To improve training quality, CPP-Net proposes a pattern learning mechanism that extracts each class's core semantic information for high-quality feature learning. At its core, it is a dynamically updated common pattern bank (CP-Bank), which stores class-specific patterns learned throughout training and serves as high-quality prior knowledge for the model. By reusing CP-Bank patterns, CPP-Net reconstructs current-stage features, reduces redundant learning of shared patterns, and boosts feature robustness and discriminability. Furthermore, an information gain-driven update strategy is proposed to ensure that the CP-Bank is aligned with the historical mean of pattern distributions, preventing excessive bias toward transient local patterns. To enhance training stability, a dynamic regulation function is designed to adaptively modulate the impact of pseudo-labels according to their confidence, thereby mitigating the adverse effects of low-confidence data. Through extensive experiments on various 2D/3D medical image segmentation datasets, CPP-Net demonstrates its effectiveness and generalizability, and achieves 7.5% mean Dice improvement over SOTA.

Deep learning has achieved significant advancements in medical image segmentation, but existing models still face challenges in accurately segmenting lesion regions. The main reason is that some lesion regions in medical images have unclear boundaries, irregular shapes, and small tissue density differences, leading to label ambiguity. However, the existing model treats all data equally without taking quality differences into account in the training process, resulting in noisy labels negatively impacting model training and unstable feature representations. In this paper, a data-driven alternating learning (DALE) paradigm is proposed to optimize the model’s training process, achieving stable and high-precision segmentation. The paradigm focuses on two key points: (1) reducing the impact of noisy labels, and (2) calibrating unstable representations. To mitigate the negative impact of noisy labels, a loss consistency-based collaborative optimization method is proposed, and its effectiveness is theoretically demonstrated. Specifically, the label confidence parameters are introduced to dynamically adjust the influence of labels of different confidence levels during model training, thus reducing the influence of noise labels. To calibrate the learning bias of unstable representations, a distribution alignment method is proposed. This method restores the underlying distribution of unstable representations, thereby enhancing the discriminative capability of fuzzy region representations. Extensive experiments on various benchmarks and model backbones demonstrate the superiority of the DALE paradigm, achieving an average performance improvement of up to 7.16%.

Driven by the rapidly growing demands of modern applications, RDMA technology has been widely adopted in cluster networks, including high-performance computing (HPC) systems and datacenters. However, the high-speed, kernel-bypass nature of RDMA presents significant challenges for congestion control (CC). On one hand, since most RDMA operations are offloaded to the RDMA network interface card (RNIC), software-based CC struggles to regulate packet transmission effectively. On the other hand, RDMA workloads are dominated by short messages, making it difficult for both reactive and proactive CC protocols to manage congestion in a timely and effective manner, posing risks to network application QoS. Despite these challenges, proactive and reactive CC protocols each have distinct strengths, and combining them offers a promising research direction. This paper proposes an endpoint-network collaborative congestion control protocol (HNCC) that integrates proactive endpoint congestion control with reactive network monitoring and adjustment to achieve precise and rapid congestion management. HNCC introduces a novel reservation mechanism embedded within the RDMA protocol to prevent endpoint congestion while efficiently mitigating in-network congestion by dynamically adjusting the data injection rate based on network trend analysis. HNCC is offloaded to the RNIC, ensuring that its traffic scheduling is timely and effective. Extensive load testing across various scales shows that HNCC achieves performance comparable to state-of-the-art CC protocols in datacenter workloads and reduces the average flow completion time (FCT) by 5 − 56% under HPC workloads.

Superpixel generation is increasingly an important area for computer vision tasks. While superpixels with highly regular shapes are preferred to make the subsequent processing easier, the accuracy of the superpixel boundaries is also necessary. Previous methods usually depend on a distance function considering both spatial and color coherency regularization on the whole image, which however is hard to balance between shape regularity and boundary adherence, especially when the desired number of superpixels is small. In addition, non-adaptive parameters and insufficient contour information also affect the performance of segmentation. To mitigate these problems, we propose a robust divide-and-conquer superpixel segmentation method, of which the core idea is that we apply a new contour information extraction and a pixel clustering to separate the input image into flat and non-flat regions, where the former targets shape regularity and the latter emphasizes boundary adherence, followed by an efficient hierarchical merging to clean up tiny and dangling superpixels. Our algorithm requires no additional parameter tuning except the desired number of superpixels since our internal parameters are self-adaptive to the image contents. Experimental results demonstrate that for public benchmark datasets, our algorithm consistently generates more regular superpixels with stronger boundary adherence than state-of-the-art methods while maintaining a competitive efficiency. The source code is available athttps://github.com/YunyangXu/HQSGRD.