Approximated RPCA for fast and efficient recovery of corrupted and linearly correlated images and video frames

This paper presents an approximated Robust Principal Component Analysis (ARPCA) framework for recovery of a set of linearly correlated images. Our algorithm seeks an optimal solution for decomposing a batch of realistic unaligned and corrupted images as the sum of a low-rank and a sparse corruption matrix, while simultaneously aligning the images according to the optimal image transformations. This extremely challenging optimization problem has been reduced to solving a number of convex programs, that minimize the sum of Frobenius norm and the l1-norm of the mentioned matrices, with guaranteed faster convergence than the state-of-the-art algorithms. The efficacy of the proposed method is verified with extensive experiments with real and synthetic data

Efficient Convolution and Transformer-Based Network for Video Frame Interpolation

Video frame interpolation is an increasingly important research task with several key industrial applications in the video coding, broadcast and production sectors. Recently, transformers have been introduced to the field resulting in substantial performance gains. However, this comes at a cost of greatly increased memory usage, training and inference time. In this paper, a novel method integrating a transformer encoder and convolutional features is proposed. This network reduces the memory burden by close to 50% and runs up to four times faster during inference time compared to existing transformer-based interpolation methods. A dual-encoder architecture is introduced which combines the strength of convolutions in modelling local correlations with those of the transformer for long-range dependencies. Quantitative evaluations are conducted on various benchmarks with complex motion to showcase the robustness of the proposed method, achieving competitive performance compared to state-of-the-art interpolation networks.Comment: Paper accepted in IEEE ICIP 2023: International Conference on Image Processing 202