Unpaired image restoration (UIR) is a significant task due to the difficulty of acquiring paired degraded/clear images with identical backgrounds. In this paper, we propose a novel UIR method based on the assumption that an image contains both degradation-related features, which affect the level of degradation, and degradation-unrelated features, such as texture and semantic information. Our method aims to ensure that the degradation-related features of the restoration result closely resemble those of the clear image, while the degradation-unrelated features align with the input degraded image. Specifically, we introduce a Feature Orthogonalization Module optimized on Stiefel manifold to decouple image features, ensuring feature uncorrelation. A task-driven Depth-wise Feature Classifier is proposed to assign weights to uncorrelated features based on their relevance to degradation prediction. To avoid the dependence of the training process on the quality of the clear image in a single pair of input data, we propose to maintain several degradation-related proxies describing the degradation level of clear images to enhance the model's robustness. Finally, a weighted PatchNCE loss is introduced to pull degradation-related features in the output image toward those of clear images, while bringing degradation-unrelated features close to those of the degraded input.

Recently, virtual staining has emerged as a promising alternative to revolutionize histological staining by digitally generating stains. However, most existing methods suffer from the curse of staining unreality and unreliability. In this paper, we propose the Orthogonal Decoupling Alignment Generative Adversarial Network (ODA-GAN) for unpaired virtual immunohistochemistry (IHC) staining. Our approach is based on the assumption that an image consists of IHC staining-related features, which influence staining distribution and intensity, and staining-unrelated features, such as tissue morphology. Leveraging a pathology foundation model, we first develop a weakly-supervised segmentation pipeline as an alternative to expert annotations. We introduce an Orthogonal MLP (O-MLP) module to project image features into an orthogonal space, decoupling them into staining-related and unrelated components. Additionally, we propose a Dual-stream PatchNCE (DPNCE) loss to resolve contrastive learning contradictions in the staining-related space, thereby enhancing staining accuracy. To further improve realism, we introduce a Multi-layer Domain Alignment (MDA) module to bridge the domain gap between generated and real IHC images. Evaluations on three benchmark datasets show that our ODA-GAN reaches state-of-the-art (SOTA) performance. Our source code is available at https://github.com/ittong/ODA-GAN.

Early smoke segmentation (ESS) enables the accurate identification of smoke sources, facilitating the prompt extinguishing of fires and preventing large-scale gas leaks. But ESS poses greater challenges than conventional object and regular smoke segmentation due to its small scale and transparent appearance, which can result in high miss detection rate and low precision. To address these issues, a Focus and Separation Network (FoSp) is proposed. We first introduce a Focus module employing bidirectional cascade which guides low-resolution and high-resolution features towards mid-resolution to locate and determine the scope of smoke, reducing the miss detection rate. Next, we propose a Separation module that separates smoke images into a pure smoke foreground and a smoke-free background, enhancing the contrast between smoke and background fundamentally, improving segmentation precision. Finally, a Domain Fusion module is developed to integrate the distinctive features of the two modules which can balance recall and precision to achieve high F_beta. Futhermore, to promote the development of ESS, we introduce a high-quality real-world dataset called SmokeSeg, which contains more small and transparent smoke than the existing datasets. Experimental results show that our model achieves the best performance on three available smoke segmentation datasets: SYN70K (mIoU: 83.00%), SMOKE5K (F_beta: 81.6%) and SmokeSeg (F_beta: 72.05%). The code can be found at https://github.com/LujianYao/FoSp.

Smoke segmentation is of great importance in precisely identifying the smoke location, enabling timely fire rescue and gas leak detection. However, due to the visual diversity and blurry edges of the non-grid smoke, noisy labels are almost inevitable in large-scale pixel-level smoke datasets. Noisy labels significantly impact the robustness of the model and may lead to serious accidents. Nevertheless, currently, there are no specific methods for addressing noisy labels in smoke segmentation. Smoke differs from regular objects as its transparency varies, causing inconsistent features in the noisy labels. In this paper, we propose a conditional sample weighting (CoSW). CoSW utilizes a multi-prototype framework, where prototypes serve as prior information to apply different weighting criteria to the different feature clusters. A novel regularized within-prototype entropy (RWE) is introduced to achieve CoSW and stable prototype update. The experiments show that our approach achieves SOTA performance on both real-world and synthetic noisy smoke segmentation datasets.

In the image dehazing task, the haze density is a key feature that affects the performance of dehazing methods. The haze density difference, which has rarely been utilized in previous methods, can guide networks to perceive different global densities and focus on local areas with high density or that are difficult to dehaze. In this paper, we propose a density-aware dehazing method named the Density Feature Refinement Network (DFR-Net), which extracts haze density features from density differences and leverages density differences to refine density features. In DFR-Net, we first generate a proposal image that has a lower overall density than the hazy input, resulting in global density differences. Additionally, the dehazing residual of the proposal image reflects the level of dehazing performance and provides local density differences that indicate localized hard dehazing or high-density areas. Subsequently, we introduce a Global Branch (GB) and a Local Branch (LB) to achieve density awareness. In GB, we use Siamese networks for feature extraction of hazy inputs and proposal images, and we propose a Global Density Feature Refinement (GDFR) module that can refine features by pushing features with different global densities further away. In LB, we explore local density features from the dehazing residuals between hazy inputs and proposal images and introduce an Intermediate Dehazing Residual Feedforward (IDRF) module to update local features and pull them close to clear image features. Sufficient experiments demonstrate that the proposed method outperforms state-of-the-art methods on various datasets.

Undoubtedly, the object is the primary factor in 3D single-object tracking (SOT) tasks. However, prior Siamese-based trackers overlook the adverse effects resulting from randomly dropped object points during backbone sampling, hindering the prediction of accurate bounding boxes (BBoxes). Therefore, developing an approach that maximizes the preservation of object points and their object-aware features is of the utmost significance. To address this, we propose an object-preserving Siamese network (OPSNet) that can effectively maintain object integrity and boost tracking performance. First, anobject highlighting moduleamplifies the object-aware features and extracts discriminative features from the template and search area. Next,object-preserving samplingselects object candidates, obtains object-preserving search area seeds, and discards background points that have less impact on tracking. Finally, anobject localization networkaccurately locates 3D BBoxes based on the object-preserving search area seeds. Extensive experiments demonstrate that the performance of OPSNet exceeds the state-of-the-art performance, achieving success gains of$\sim$9.4% and$\sim$2.5% on the KITTI and Waymo Open datasets, respectively.

Domain Name System (DNS) is a widely used infrastructure for remote control and batch management of IoT devices. As a critical Internet infrastructure, DNS is structured as a tree‐like hierarchy with single root zone authority at the top, which puts the operation of DNS at risk from single point of failure. The current root zone management is lack of transparency and accountability, since only the root zone file is published as the final outcome of operations inside the root zone authority. Towards distributed root zone operation in DNS, this paper presents a blockchain‐based root operation architecture—RootChain, composed of multiple root servers. On the basis of maintaining the single root authority for top‐level domain (TLD), RootChain decentralizes TLD data publication by empowering delegated TLD authorities to publish authenticated data directly. The transparency and accountability of root zone operation are attained by smart‐contracting the whole life cycle of TLD operation and logging all operations on the chain. RootChain is transparent to recursive/stub resolver and DNS/DNSSEC‐compatible. A proof‐of‐concept prototype of RootChain has been implemented with Hyperledger Fabric and evaluated by experiments.