Paper ID: FSDM2158

Presenter: Wenwu Wang, University of Surrey, UK

Title of Presentation: Sparse Analysis Model Based Dictionary Learning and Signal Reconstruction

Abstract of Presentation: Sparse representation has been studied extensively in the past decade in a variety of applications, such as denoising, source separation and classification. Earlier effort has been focused on the well-known synthesis model, where a signal is decomposed as a linear combination of a few atoms of a dictionary. However, the analysis model, a counterpart of the synthesis model, has not received much attention until recent years. The analysis model takes a different viewpoint to sparse representation, and it assumes that the product of an analysis dictionary and a signal is sparse. Compared with the synthesis model, this model tends to be more expressive to represent signals, as a much richer union of subspaces can be described. This talk focuses on the analysis model and aims to discuss the two main challenges: analysis dictionary learning (ADL) and signal reconstruction.

In the ADL problem, the dictionary is learned from a set of training samples so that the signals can be represented sparsely based on the analysis model, thus offering the potential to fit the signals better than pre-defined dictionaries. Among the existing ADL algorithms, such as the well-known Analysis K-SVD, the dictionary atoms are updated sequentially. The first part of this talk presents two novel analysis dictionary learning algorithms to update the atoms simultaneously. In particular, by adapting Simultaneous Codeword Optimization (SimCO), an algorithm proposed originally for the synthesis model, the Analysis SimCO algorithm is developed for the analysis model. This algorithm assumes that the analysis dictionary contains unit l2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to the existing ADL algorithms, the dictionary learned by Analysis SimCO may contain similar atoms. To address this issue, Incoherent Analysis SimCO is proposed by employing a coherence constraint and introducing a decorrelation step to enforce this constraint. The competitive performance of the proposed algorithms is demonstrated in the experiments for recovering synthetic dictionaries and removing additional noise in images, as compared with existing methods.

The second part of this talk discusses how to reconstruct signals with learned dictionaries under the analysis model. This is demonstrated by a challenging application problem: multiplicative noise removal (MNR) of images. In the existing sparsity motivated methods, the MNR problem is addressed using pre-defined dictionaries, or learned dictionaries based on the synthesis model. In this talk, the potential of analysis dictionary learning for the MNR problem is discussed. Two new MNR algorithms using analysis dictionary learning are presented. In the first algorithm, a dictionary learned based on the analysis model is employed to form a regularization term, which can preserve image details while removing multiplicative noise. In the second algorithm, a smoothness regularizer is introduced to the reconstruction formulation to improve the recovery quality of smooth areas in images. This regularizer can be seen as an enhanced Total Variation (TV) term with an additional parameter controlling the level of smoothness. To address the optimization problem of this model, the Alternating Direction Method of Multipliers (ADMM) is adapted and a relaxation technique is developed to allow variables to be updated flexibly. Experimental results show the superior performance of the proposed algorithms as compared with three recently proposed algorithms for a range of noise levels.