# A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution

arXiv: https://arxiv.org/abs/1304.5319 · HTML: https://mlsanity.com/paper/1304.5319/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2013.

## Built on by (most cited first, 6 of 6)

- [Discrete Cosine Transform Network for Guided Depth Map Super-Resolution](https://mlsanity.com/paper/2104.06977/) (2021)
- [Joint Implicit Image Function for Guided Depth Super-Resolution](https://mlsanity.com/paper/2107.08717/) (2021)
- [Improving Monocular Visual Odometry Using Learned Depth](https://mlsanity.com/paper/2204.01268/) (2022)
- [Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution](https://mlsanity.com/paper/2411.03239/) (2024)
- [Depth Super-Resolution from Explicit and Implicit High-Frequency Features](https://mlsanity.com/paper/2303.09307/) (2023)
- [Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network](https://mlsanity.com/paper/2404.03930/) (2024)

## Abstract (arXiv)

High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene. To that end, we introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity and depth information. This model is based on the assumption that the co-supports of corresponding bimodal image structures are aligned when computed by a suitable pair of analysis operators. No analytic form of such operators exist and we propose a method for learning them from a set of registered training signals. This learning process is done offline and returns a bimodal analysis operator that is universally applicable to natural scenes. We use this to exploit the bimodal co-sparse analysis model as a prior for solving inverse problems, which leads to an efficient algorithm for depth map super-resolution.