Discrete Cosine Transform Network for Guided Depth Map Super-Resolution (2104.06977v3)

Abstract: Guided depth super-resolution (GDSR) is an essential topic in multi-modal image processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones collected with suboptimal conditions with the help of HR RGB images of the same scene. To solve the challenges in interpreting the working mechanism, extracting cross-modal features and RGB texture over-transferred, we propose a novel Discrete Cosine Transform Network (DCTNet) to alleviate the problems from three aspects. First, the Discrete Cosine Transform (DCT) module reconstructs the multi-channel HR depth features by using DCT to solve the channel-wise optimization problem derived from the image domain. Second, we introduce a semi-coupled feature extraction module that uses shared convolutional kernels to extract common information and private kernels to extract modality-specific information. Third, we employ an edge attention mechanism to highlight the contours informative for guided upsampling. Extensive quantitative and qualitative evaluations demonstrate the effectiveness of our DCTNet, which outperforms previous state-of-the-art methods with a relatively small number of parameters. The code is available at \url{https://github.com/Zhaozixiang1228/GDSR-DCTNet}.

• The paper presents DCTNet, which integrates discrete cosine transform into a deep learning framework for improved guided depth map super-resolution.
• It employs semi-coupled feature extraction and guided edge spatial attention to effectively capture cross-modal features and prevent RGB texture over-transfer.
• Extensive evaluations on datasets like NYU v2 demonstrate that DCTNet achieves state-of-the-art RMSE performance with fewer parameters, benefiting applications such as autonomous driving and AR.

An Analysis of the Discrete Cosine Transform Network for Guided Depth Map Super-Resolution

The paper "Discrete Cosine Transform Network for Guided Depth Map Super-Resolution" introduces DCTNet, a novel approach aimed at enhancing guided depth super-resolution (GDSR) by tackling key issues such as cross-modal feature extraction and RGB texture over-transfer. The fundamental innovation of DCTNet lies in the unique integration of Discrete Cosine Transform (DCT) within a deep learning framework, presenting a hybrid model that bridges traditional optimization and modern deep learning methodologies. This work contributes to the growing field of multi-modal image processing, where the challenge is to reconstruct high-resolution depth maps from low-resolution counterparts with the aid of high-resolution RGB images.

Key Components and Methodology

DCTNet is structured around four core components that together enhance its capability for depth map reconstruction:

1. Semi-Coupled Feature Extraction (SCFE): This module employs semi-coupled residual blocks that utilize both shared and private convolutional kernels to extract cross-modal features. This design allows joint feature learning while maintaining modality-specific information, which is crucial for effective texture and structure extraction across depth and RGB images.
2. Guided Edge Spatial Attention (GESA): Utilizing an enhanced spatial attention (ESA) mechanism, this module selectively emphasizes edge information in RGB images pertinent to depth map reconstruction. This mitigates the risk of RGB texture over-transfer, which is a common pitfall in GDSR tasks.
3. Discrete Cosine Transform Module: The inclusion of DCT into the network allows the model to solve a formulated optimization problem directly within the feature space, enhancing depth feature reconstruction. Notably, the use of DCT, a method rooted in signal processing, confers greater interpretability and reduces dependency on learnable parameters by optimizing only key transformation parameters.
4. Depth Reconstruction (DR): This final stage synthesizes the high-resolution depth map from the enhanced features processed in earlier stages.

Performance and Implications

The efficacy of DCTNet is substantiated through extensive evaluations across well-regarded datasets (e.g., NYU v2, Middlebury, Lu, and RGBDD), showing that it reaches or surpasses contemporary state-of-the-art methods in GDSR in terms of RMSE across various scale factors (×4 to ×16). Noteworthy, DCTNet performs especially well with relatively few parameters, underscoring its efficient design.

The paper's empirical results suggest practical implications for industries relying on depth map estimation, such as autonomous driving and augmented reality, where real-time and high-accuracy depth perception is necessary. The model's ability to improve edge recovery and minimize texture misalignment makes it particularly suitable for applications in less controlled environments where depth sensors often operate below optimal conditions.

Future Directions

While the paper delivers significant advances, future work may explore further simplification of the DCT integration to refine computational efficiency without compromising performance. Additionally, the robustness of DCTNet under diverse environmental conditions, including poor lighting or occlusions in RGB images, could be investigated to enhance its applicability in real-world scenarios. Moreover, extending DCTNet's architecture to other multi-modal processing tasks would be a worthwhile exploration, potentially offering improvements in domains such as stereo vision and 3D reconstruction.

In conclusion, this work propels guided depth map super-resolution forward with a compelling hybrid approach that leverages the strengths of both traditional signal processing techniques and deep learning.