TY - GEN
T1 - Poisson noise removal via learned dictionary
AU - Xiao, Yu
AU - Zeng, Tieyong
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - In this paper, we address the restoration of images corrupted by Poisson noise. The proposed new model contains two terms: one is from the sparse representation of the transformed image via variance stabilizing transformation (VST); the other is a data-fidelity term caused by the statistical properties of Poisson noise. The main algorithm is efficient. We first learn a dictionary to sparsely represent the transformed image using a state-of-the-art dictionary learning method, and then solve the minimization of the variational form by Newton method. Comparative experiments are carried out to show the leading performance of our new model.
AB - In this paper, we address the restoration of images corrupted by Poisson noise. The proposed new model contains two terms: one is from the sparse representation of the transformed image via variance stabilizing transformation (VST); the other is a data-fidelity term caused by the statistical properties of Poisson noise. The main algorithm is efficient. We first learn a dictionary to sparsely represent the transformed image using a state-of-the-art dictionary learning method, and then solve the minimization of the variational form by Newton method. Comparative experiments are carried out to show the leading performance of our new model.
KW - Dictionary learning
KW - Image denoising
KW - Poisson noise
KW - Sparse representations
KW - Variance stabilizing transformation
UR - http://www.scopus.com/inward/record.url?scp=78651093870&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2010.5651863
DO - 10.1109/ICIP.2010.5651863
M3 - Conference proceeding
AN - SCOPUS:78651093870
SN - 9781424479948
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1177
EP - 1180
BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010
Y2 - 26 September 2010 through 29 September 2010
ER -