Learning Hierarchical Color Guidance for Depth Map Super-Resolution
Not on the map. Why?
Abstract (arXiv)
Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network to achieve DSR. On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process by using a semantic mask that encodes the global guidance information. The color information of these two dimensions plays a role in the front and back ends of the attention-based feature projection (AFP) module in a more comprehensive form. Simultaneously, the AFP module integrates the multi-scale content enhancement block and adaptive attention projection block to make full use of multi-scale information and adaptively project critical restoration information in an attention manner for DSR. Compared with the state-of-the-art methods on four benchmark datasets, our method achieves more competitive performance both qualitatively and quantitatively.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 23 of its 73 references that are in the map. First posted on arXiv 12 Mar 2024.
Builds on
23 of 73 references.
References · 23
- 2018CBAM: Convolutional Block Attention Modulecited by 2,469
- 2018Residual Dense Network for Image Super-Resolutioncited by 798
- 2017Dilated Residual Networkscited by 294
- 2018Deep Back-Projection Networks For Super-Resolutioncited by 225
- 2021Discrete Cosine Transform Network for Guided Depth Map Super-Resolutioncited by 71
- 2021Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baselinecited by 61
- 2019Deformable Kernel Networks for Joint Image Filteringcited by 59
- 2017Joint Image Filtering with Deep Convolutional Networkscited by 52
- 2020RGB-D Salient Object Detection with Cross-Modality Modulation and Selectioncited by 50
- 2020Channel Attention based Iterative Residual Learning for Depth Map Super-Resolutioncited by 40
- 2022CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detectioncited by 38
- 2022Does Thermal Really Always Matter for RGB-T Salient Object Detection?cited by 37
11 more
- 2021Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detectioncited by 34
- 2021RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Imagescited by 34
- 2021Joint Implicit Image Function for Guided Depth Super-Resolutioncited by 31
- 2021BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimationcited by 31
- 2021Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolutioncited by 30
- 2023Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detectioncited by 25
- 2016ATGV-Net: Accurate Depth Super-Resolutioncited by 22
- 2022A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labelscited by 20
- 2022Learning Detail-Structure Alternative Optimization for Blind Super-Resolutioncited by 6
- 2018Deeply Supervised Depth Map Super-Resolution as Novel View Synthesiscited by 5
- 2021Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imagingcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.