App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Depth reliability-aware complementary fusion network for high-resolution dichotomous image segmentation

Yao Jialiang
Yang Haiying
Deng Jianguo
Tu Chiyu
School of Electronics & Information Engineering, Liaoning University of Technology, Jinzhou Liaoning 121001, China
DOI: 10.19734/j.issn.1001-3695.2026.06.0186

Abstract

To address the issue that local structural distortions easily contaminate visual features and weaken structural constraints when introducing monocular pseudo-depth maps as geometric priors in high-resolution dichotomous image segmentation (DIS) , this paper proposed a depth reliability-aware complementary fusion network (DRCFNet) . The network designed a depth reliability assessment (DRA) module to finely characterize the reliability of pseudo-depth at different spatial locations. Furthermore, the study constructed a bidirectional complementary gating (BCG) mechanism to suppress erroneous responses and compensate for RGB features in depth-unreliable regions, achieving adaptive redistribution of cross-modal information. Meanwhile, the model introduced a reliability-weighted integrity loss to alleviate optimization conflicts. Experimental results on the DIS-5K dataset showed that, compared with PDFNet, DRCFNet improved the weighted F-measure (Fβw) from 0.850 to 0.867 and reduced the mean absolute error (MAE) from 0.041 to 0.037 on the most challenging DIS-TE4 subset; on the combined results of the four test subsets, Fβw increased to 0.870 and the MAE decreased to 0.031. These results demonstrate that the proposed network effectively utilizes locally defective pseudo-depth priors to improve segmentation accuracy and structural integrity in complex scenes.

Foundation Support

辽宁省科学技术计划项目面上项目(2025MS163)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.06.0186
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-23] Accepted Paper

Cite This Article

姚嘉梁, 杨海英, 邓建国, 等. 面向高分辨率二值图像分割深度可靠性感知互补融合网络 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0186. (Yao Jialiang, Yang Haiying, Deng Jianguo, et al. Depth reliability-aware complementary fusion network for high-resolution dichotomous image segmentation [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0186. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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