Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization

by   Yanwei Zhao, et al.

In image denoising (IDN) processing, the low-rank property is usually considered as an important image prior. As a convex relaxation approximation of low rank, nuclear norm based algorithms and their variants have attracted significant attention. These algorithms can be collectively called image domain based methods, whose common drawback is the requirement of great number of iterations for some acceptable solution. Meanwhile, the sparsity of images in a certain transform domain has also been exploited in image denoising problems. Sparsity transform learning algorithms can achieve extremely fast computations as well as desirable performance. By taking both advantages of image domain and transform domain in a general framework, we propose a sparsity transform learning and weighted singular values minimization method (STLWSM) for IDN problems. The proposed method can make full use of the preponderance of both domains. For solving the non-convex cost function, we also present an efficient alternative solution for acceleration. Experimental results show that the proposed STLWSM achieves improvement both visually and quantitatively with a large margin over state-of-the-art approaches based on an alternatively single domain. It also needs much less iteration than all the image domain algorithms.



There are no comments yet.


page 10

page 12

page 14


Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction

Low rank matrix approximation (LRMA), which aims to recover the underlyi...

ℓ_0 Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees

Many applications in signal processing benefit from the sparsity of sign...

Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising

Nonlocal image representation or group sparsity has attracted considerab...

Image Denoising Using Low Rank Minimization With Modified Noise Estimation

Recently, the application of low rank minimization to image denoising ha...

tfShearlab: The TensorFlow Digital Shearlet Transform for Deep Learning

The shearlet transform from applied harmonic analysis is currently the s...

Multi-band Weighted l_p Norm Minimization for Color and Multispectral Image Denoising

Low rank matrix approximation (LRMA) has drawn increasing attention in r...

Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds

As a significant subspace clustering method, low rank representation (LR...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.