Abstract-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 1-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.
Ebadi REF presented an approximated RPCA framework for recovery of a set of linearly correlated images seeking 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.
Approximated RPCA for fast and efficient recovery of corrupted and linearly correlated images and video frames
2015 International Conference on Systems, Signals and Image Processing (IWSSIP)
2015 International Conference on Systems, Signals and Image Processing (IWSSIP)
Computer Science