A popular representative of this approach is the Iterative Shrinkage-Thresholding Algorithm (ISTA) and its learned version -- LISTA, aiming for the sparse representations of the processed signals.
Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data.
Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing.
Ranked #1 on Color Image Denoising on BSD68 sigma75
The proposed method adds controlled noise to the input and estimates a sparse representation from the perturbed signal.