We propose a channel-correlation adaptive dictionary learning based demosaicking (CADLD).
We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks.
A globally variance-constrained sparse representation (GVCSR) model is proposed in this paper, where a variance-constrained rate term is introduced to the optimization process.
In this paper, we consider optimally designing CFAs for sparse representation-based demosaicking, where the dictionary is well-chosen.
We review some typical algorithms,Iteratively Reweighted ℓ1 minimization(IRL1),Iteratively Reweighted Least Squares(IRLS) (and its general form General Iteratively Reweighted Least Squares(GIRLS)), and Iteratively Thresholding Method(ITM), forℓp minimization and do comprehensive comparison among them, in which IRLS is identified as having the bestperformance and being the fastest as well.
We propose an algorithm, called Bases Sorting, to sort the bases of over-complete dictionaries used in sparse representation according to the magnitudes of coefficients when representing the training samples.