We propose a new plug-and-play image restoration method based on primal-dual splitting.
Existing plug-and-play image restoration methods interpret any off-the-shelf Gaussian …

The total variation (TV) regularization has been widely used in various applications related
to hyperspectral (HS) signal and image processing due to its potential in modeling the …

Existing regularization functions for color image restoration cannot remove color artifact,
undesirable appearance of colors, sufficiently, which degrades the quality of color images. In …

We propose a new regularization technique, named Hybrid Spatio-Spectral Total Variation
(HSSTV), for hyperspectral image (HSI) restoration. Popular regularization techniques for …

We propose a new regularization function, named Spatio-Spectral Structure Tensor Total
Variation (S 3 TTV), for hyperspectral image (HSI) denoising. Spatio-Spectral Total Variation …

This paper proposes an epigraphical relaxation (ERx) technique for non-proximable mixed
norm minimization. Mixed norm regularization methods play a central role in signal …

This paper proposes a novel regularization method, named Spatio-Spectral Structure
Tensor Total Variation (S3TTV), for denoising and destriping of hyperspectral (HS) images …

This paper proposes a precise signal recovery method with multilayered non-convex
regularization, enhancing sparsity/low-rankness for high-dimensional signals including …

We propose a regularization function for hyperspectral image restoration based on a newly-
designed structure tensor. We adopt a convex optimization approach with the use of the …

This paper proposes an epigraphically-relaxed linearly-involved generalized Moreau-
enhanced (ER-LiGME) model for layered mixed norm regularization. Group sparse and low …