TY - JOUR
T1 - A novel two-stage impulse noise removal technique based on neural networks and fuzzy decision
AU - Liang, Sheng Fu
AU - Lu, Shih Mao
AU - Chang, Jyh Yeong
AU - Lin, Chin Teng
N1 - Funding Information:
Manuscript received August 14, 2006; revised May 30, 2007; accepted July 29, 2007. This work was supported in part by the National Science Council, Taiwan, under Grant NSC 95-2221-E-009-210, Grant NSC 96-2221-E-009-058, and Grant NSC 95-2752-E-009-011-PAE.
PY - 2008
Y1 - 2008
N2 - In this paper, a novel two-stage noise removal algorithm to deal with impulse noise is proposed. In the first stage, an adaptive two-level feedforward neural network (NN) with a backpropagation training algorithm was applied to remove the noise cleanly and keep the uncorrupted information well. In the second stage, the fuzzy decision rules inspired by the human visual system (HVS) are proposed to classify the image pixels into human perception sensitive class and nonsensitive class, and to compensate the blur of the edge and the destruction caused by the median filter. An NN is proposed to enhance the sensitive regions with higher visual quality. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and smoothness in edge regions.
AB - In this paper, a novel two-stage noise removal algorithm to deal with impulse noise is proposed. In the first stage, an adaptive two-level feedforward neural network (NN) with a backpropagation training algorithm was applied to remove the noise cleanly and keep the uncorrupted information well. In the second stage, the fuzzy decision rules inspired by the human visual system (HVS) are proposed to classify the image pixels into human perception sensitive class and nonsensitive class, and to compensate the blur of the edge and the destruction caused by the median filter. An NN is proposed to enhance the sensitive regions with higher visual quality. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and smoothness in edge regions.
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U2 - 10.1109/TFUZZ.2008.917297
DO - 10.1109/TFUZZ.2008.917297
M3 - Article
AN - SCOPUS:50849144920
SN - 1063-6706
VL - 16
SP - 863
EP - 873
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 4
ER -