Super-Resolution of Multi-Observed RGB-D Images Based on Nonlocal Regression and Total Variation.

There is growing demand for accuracy in image processing and visualization, and the super-resolution (SR) technique for multi-observed RGB-D images has become popular, because it provides space-redundant information and produces a detailed reconstruction even with a large magnification factor. This technique has been thoroughly investigated in recent years. Nevertheless, technical challenges remain, such as finding sub-pixel correspondences with low-resolution (LR) observations, exploiting space-redundant information, formulating space homogeneity constraints, and leveraging cross-image similarities in structures. To address these challenges, this paper proposes a unified optimization framework to estimate both the super-resolved RGB image and the super-resolved depth image from the multi-observed LR RGB-D images using their correlations. Using depth-assisted cross-image correspondences, the RGB image SR problem is formulated as an effective regularization function by incorporating the normalized bilateral total variation regularizer, and it is efficiently solved by a first-order primal-dual algorithm. The depth image SR estimate can be obtained by minimizing a nonlocal regression-based energy, which integrates the structural cues of the super-resolved RGB image in a detail-preserving fashion. Essentially, our unified optimization framework uses the RGB image and depth image as a priori knowledge that the SR process uses for better accuracy. Our extensive experiments on public RGB-D benchmarks and real data and our quantitative comparison with several state-of-the-art methods demonstrate the superiority of our method in terms of accuracy, versatility, and reliability of details and sharp feature preservation.