Online Simultaneous Learning Projection Matrix and Sparsifying Dictionary
نویسنده
چکیده
An online algorithm is proposed in this letter to optimize the Projection Matrix and Sparsifying Dictionary (PMSD) simultaneously on a large training dataset. A closedform solution is derived for optimizing the projection matrix with a fixed sparsifying dictionary and the stochastic method is used to optimize the sparsifying dictionary with a fixed optimized projection matrix on a large training dataset to form the proposed online algorithm. Benefiting from training on a large dataset, the proposed algorithm yields a much better performance in terms of signal recovery accuracy than the existing ones. The simulation results on natural images demonstrate its effectiveness and efficiency compared with the existing methods.
منابع مشابه
Online Learning Sensing Matrix and Sparsifying Dictionary Simultaneously for Compressive Sensing
This paper considers simultaneously optimizing the Sensing Matrix and Sparsifying Dictionary (SMSD) on a large training dataset. We propose an online algorithm that consists of a closed-form solution for optimizing the sensing matrix with a fixed sparsifying dictionary and a stochastic method for optimizing the sparsifying dictionary on a large training dataset when the sensing matrix is fixed....
متن کاملVIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Techniques exploiting the sparsity of images in a transform domain have been effective for various applications in image and video processing. Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction. Recently, we proposed methods for online learning of sparsifying transforms from st...
متن کاملLEARNING TO SENSE SPARSE SIGNALS: SIMULTANEOUS SENSING MATRIX AND SPARSIFYING DICTIONARY OPTIMIZATION By
Sparse signals representation, analysis, and sensing, has received a lot of attention in recent years from the signal processing, optimization, and learning communities. On one hand, the learning of overcomplete dictionaries that facilitate a sparse representation of the image as a liner combination of a few atoms from such dictionary, leads to state-of-the-art results in image and video restor...
متن کاملMicrowave Radiation Image Reconstruction Method Based on Adaptive Multi-structural Dictionary Learning
Due to the complicated structure of microwave radiometric imaging system and the massive amount of data collection in one snapshot, it is difficult to achieve the high spatial resolution image by conventional microwave radiation imaging method based on the Nyquist sampling. In this paper, according to the priori information of the compressible multi-structural information of microwave radiation...
متن کاملℓ0 Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees
Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary. Synthesis sparsifying dictionaries that are directly adapted to data have been popular in applications such as image denoising, inpainting, and medical image reconstruction. In this work, we focus instead on the sparsifying transform model, and study the learning of well-cond...
متن کامل