Low rank prior in single patches for non-pointwise impulse noise removal

TY - JOUR

T1 - Low rank prior in single patches for non-pointwise impulse noise removal

AU - Wang, Ruixuan

AU - Pakleppa, Markus

AU - Trucco, Emanuele

N1 - © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2015

Y1 - 2015

N2 - This paper introduces a low rank prior in small oriented noise-free image patches: Considering an oriented patch as a matrix, a low-rank matrix approximation is enough to preserve the texture details in the optimally oriented patch. Based on this prior, we propose a single-patch method within a generalized joint low-rank and sparse matrix recovery framework to simultaneously detect and remove non-pointwise random-valued impulse noise (e.g., very small blobs). A weighting matrix is incorporated in the framework to encode an initial estimate of the spatial noise distribution. An accelerated proximal gradient method is adapted to estimate the optimal noise-free image patches. Experiments show the effectiveness of our framework in detecting and removing non-pointwise random-valued impulse noise.

AB - This paper introduces a low rank prior in small oriented noise-free image patches: Considering an oriented patch as a matrix, a low-rank matrix approximation is enough to preserve the texture details in the optimally oriented patch. Based on this prior, we propose a single-patch method within a generalized joint low-rank and sparse matrix recovery framework to simultaneously detect and remove non-pointwise random-valued impulse noise (e.g., very small blobs). A weighting matrix is incorporated in the framework to encode an initial estimate of the spatial noise distribution. An accelerated proximal gradient method is adapted to estimate the optimal noise-free image patches. Experiments show the effectiveness of our framework in detecting and removing non-pointwise random-valued impulse noise.

KW - Approximation methods

KW - Equations

KW - Image edge detection

KW - Sparse matrices

KW - Noise reduction

KW - Noise

KW - Noise measurement

U2 - 10.1109/TIP.2015.2400225

DO - 10.1109/TIP.2015.2400225

M3 - Article

C2 - 25667352

SN - 1057-7149

VL - 24

SP - 1485

EP - 1496

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

IS - 5

ER -