Spatially adaptive image denoising using inter‐scale dependence in directionlet domain

Efficient bivariate image denoising technique using new orthogonal CWT filter design

“…In this section, we extend the bivariate shrinkage algorithm of [11], to denoise CWT systems for both Gaussian as well as salt & pepper noises. We propose to apply the bivariate shrinkage algorithm [15,16] to threshold the complex magnitudes of the real and imaginary wavelet coefficients of the CWT, at every sub-band. The noisy children r 1, n (k) and parent magnitudes r 2, n (k) at the kth subband are related to its noiseless ones, by…”

A Novel Adaptive Non-Local Means-Based Nonlinear Fitting for Visibility Improving

“…where M = −X −Q , combining Equations (19)- (21) into Equation 16, we can finally obtain the optimization Equation (22).…”

“…There are mainly three kinds of improvement directions, including denoising combined with correlation, denoising combined with singularity detection and denoising based on the coefficient threshold in the wavelet domain. Related researches mainly include: Sethunadh R.S et al [21] proposed a spatial adaptive denoising method based on directionlet transform to reduce Gaussian noise by considering the correlation of the directionlet coefficients across different scales. Norbert Remenyi et al [22] proposed an image denoising method based on 2D scale-mixing complex wavelet transforms which uses empirical Bayesian method to achieve good effect.…”

Adaptive frequency median filter for the salt and pepper denoising problem

“…In image processing [1, 2], image denoising is an essential pre‐processing task to improve image quality for other post‐processing tasks such as image segmentation, feature extraction, image analysis. Owing to its important role, it has attracted much attention [3–6]. The goal of the image denoising problem is to remove noise with preserving edges, textures, structures and other details of the image [7–9].…”