Total Bending Method for Piecewise Smoothing Image Denoising

A Multigrid Method for Total Variation-based Image Denoising

Edge-Preserving Piecewise Linear Image Smoothing Using Piecewise Constant Filters

Most image smoothing filters in the literature assume a piecewise constant model of smoothed output images. However, the piecewise constant model assumption can cause artifacts such as gradient reversals in applications such as image detail enhancement, HDR tone mapping, etc. In these applications, a piecewise linear model assumption is more preferred. In this paper, we propose a simple yet ver...

Maximum-Variance Total Variation Denoising for Interpretable Spatial Smoothing

We consider the problem of spatial regression where interpretability of the model is a high priority. Such problems appear frequently in a diverse set of fields from climatology to epidemiology to predictive policing. For cognitive, logistical, and organizational reasons, humans tend to infer regions or neighborhoods of constant value, often with sharp discontinuities between regions, and then ...

Directional Total Variation Filtering Based Image Denoising Method

In this paper, we study signal denoising technique based on total variation (TV) which was reported by Ivan W. Selesnick, Ilker Bayram [1]. Here, we present a directional total variation algorithm for image denoising. In most of the image denoising methods, the total variation denoising is directly performed on the noisy images. In this work, we apply a 1D TV denoising algorithm in sequential m...

A New Shearlet Framework for Image Denoising

Traditional noise removal methods like Non-Local Means create spurious boundaries inside regular zones. Visushrink removes too many coefficients and yields recovered images that are overly smoothed. In Bayesshrink method, sharp features are preserved. However, PSNR (Peak Signal-to-Noise Ratio) is considerably low. BLS-GSM generates some discontinuous information during the course of denoising a...