Implementation of sparse image denoising algorithm and its matlab code

Implementation of sparse image denoising algorithm and its matlab code

Sparse representation has become an important direction in image signal processing, and the most famous method is to achieve sparsity by using L1 norm constraints. In image denoising, the use of variational partial differential equations (PDE) for denoising is also a common method. This article will introduce the implementation of sparse image denoising based on the variational partial differential equation HNHOTV_OGS algorithm, and provide the corresponding matlab code.

The HNHOTV_OGS algorithm combines PDE denoising and L1 norm sparse representation. Among them, PDE can smooth the noise and preserve the edge information of the image; while the L1 norm can make the sparse representation of the signal more effective. At the same time, the OGS (Orthogonal Gradient Subsampling) algorithm is also used to accelerate calculations and improve efficiency.

The following is the matlab code implementation:

function u=HNTV_ogs(f,tau,lambda,N,n)

%通过高斯算子平滑输入图片
f=conv2