Motion-Compensated Spatio-Temporal Filtering for Multi-Image and Multimodal Super-Resolution

The classical multi-image super-resolution model assumes that the super-resolved image is related to the low-resolution frames by warping, convolution and downsampling. State-of-the-art algorithms either use explicit registration to fuse the information for each pixel in its trajectory or exploit spatial and temporal similarities. We propose to combine both ideas, making use of inter-frame motion and exploiting spatio-temporal redundancy with patch-based techniques. We introduce a non-linear filtering approach that combines patches from several frames not necessarily belonging to the same pixel trajectory. The selection of candidate patches depends on a motion-compensated 3D distance, which is robust to noise and aliasing. The selected 3D volumes are then sliced per frame, providing a collection of 2D patches which are finally averaged depending on their similarity to the reference one. This makes the upsampling strategy robust to flow inaccuracies and occlusions. Total variation and nonlocal regularization are used in the deconvolution stage. The experimental results demonstrate the state-of-the-art performance of the proposed method for the super-resolution of videos and light-field images. We also adapt our approach to multimodal sequences when some additional data at the desired resolution is available.