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Image-guided depth completion aims at generating a dense depth map from sparse light detection and ranging (LiDAR) data and the corresponding RGB image, which is crucial for applications that require 3-D scene perception, such as augmented reality and human-computer interaction. Recent methods have shown promising performance by reformulating it as a classification problem with two subtasks: depth discretization and probability prediction. They divide the depth range into several discrete depth values as depth categories, serving as priors for scene depth distributions. However, previous depth discretization methods are easy to be impacted by depth distribution variations across different scenes, resulting in suboptimal scene depth distribution priors. To address the above problem, we propose a progressive depth decoupling and modulating network, which incrementally decouples the depth range into bins and adaptively generates multiscale dense depth maps in multiple stages. Specifically, we first design a bins initializing module (BIM) to construct the seed bins by exploring the depth distribution information within a sparse depth map, adapting variations of depth distribution. Then, we devise an incremental depth decoupling branch to progressively refine the depth distribution information from global to local. Meanwhile, an adaptive depth modulating branch is developed to progressively improve the probability representation from coarse-grained to fine-grained. Also, the bidirectional information interactions are proposed to strengthen the information interaction between those two branches (subtasks) for promoting information complementation in each branch. Furthermore, we introduce a multiscale supervision mechanism to learn the depth distribution information in latent features and enhance the adaptation capability across different scenes. Experimental results on public datasets demonstrate that our method outperforms the state-of-the-art (SOTA) methods. We will release the source codes and pretrained models.

Salient object detection of surface defects is one of the surface defect detection tasks, which aims at highlighting the defect regions from the surface of strip steel, magnetic tale, road, and so on. However, the performance of existing methods degrades dramatically when dealing with complex scenarios, such as low contrast of defect regions and various defect shapes. Therefore, in this article, we propose a novel saliency model, namely, localizing, focus, and refinement network (LFRNet), which consists of the semantic-guided localizing module, the context-driven focus module, and the edge-aware refinement (ER) module. First, the semantic-guided localizing module deploys the graph reasoning (GR) unit and the global attention (GA) unit to localize the potential defect regions from a global view. Second, the context-driven focus module employs the split context (SC) unit and the mutual attention (MA) unit to perform the identification process via the introduction of spatial detail features. Finally, to further improve the accuracy of the detection results, we deploy the ER module, which introduces the boundary cues via the edge generation (EG) unit and aggregates the localizing result, the focus results, and the edge information into the high-quality detection map. Extensive experiments on four public defect datasets clearly show the effectiveness and superiority of the proposed LFRNet, where the LFRNet obtains an improvement of 4.1%, 5.7%, 1.0%, and 0.8% on F-measure (FM), weighted FM (WF), E-measure (EM), and structure-measure (SM), respectively, compared with the top-level method: AEP.

The alteration of metabolic processes has been found to have significant impacts on the development of hepatocellular carcinoma (HCC). Nevertheless, the effects of dysfunction of tyrosine metabolism on the development of HCC remains to be discovered. This research demonstrated that tyrosine hydroxylase (TH), which responsible for the initial and limiting step in the bio-generation of the neuro-transmitters dopamine and adrenaline, et al. was shown to be reduced in HCC. Increased expression of TH was found facilitates the survival of HCC patients. In addition, decreased TH indicated larger tumor size, much more numbers of tumor, higher level of AFP, and the presence of cirrhosis. TH effectively impairs the growth and metastasis of HCC cells, a process dependent on the phosphorylation of serine residues (S19/S40). TH directly binds to Smad2 and hinders the cascade activation of TGFβ/Smad signaling with the treatment of TGFβ1. In summary, our study uncovered the non-metabolic functions of TH in the development of HCC and proposes that TH might be a promising biomarker for diagnosis as well as an innovative target for metastatic HCC.