Browsing by Author "Lu, Shih-Mao"

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A HVS-directed neural-network-based approach for salt-pepper impulse noise removal
(2006-07-01)
Lu, Shih-Mao
;
Liang, Sheng-Fu
;
Lin, Chin-Teng
;
生物科技學系
;
資訊工程學系
In this paper, a novel two-stage noise removal algorithm to deal with salt-pepper impulse noise is proposed. In the first stage, the decision-based recursive adaptive noise-exclusive median filter is applied to remove the noise cleanly and to keep the uncorrupted information as well as possible. In the second stage, the fuzzy decision rules inspired by human visual system (HVS) are proposed to classify image pixels into human perception sensitive class and non-sensitive class. A neural network is proposed to compensate the sensitive regions for image quality enhancement. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions of the resultant images.
A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision
(2008-08-01)
Liang, Sheng-Fu
;
Lu, Shih-Mao
;
Chang, Jyh-Yeong
;
Lin, Chin-Teng (CT)
;
交大名義發表
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 back-propagation 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.
An HVS-Directed neural-network-based image resolution enhancement scheme for image resizing
(2007-08-01)
Lin, Chin-Teng
;
Fan, Kang-Wei
;
Pu, Her-Chang
;
Lu, Shih-Mao
;
Liang, Sheng-Fu
In this paper, a novel human visual system (HVS)directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the HVS is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. Bilinear interpolation is used to interpolate the nonsensitive regions and a neural network is proposed to interpolate the sensitive regions along edge directions. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce a higher visual quality for the interpolated image than the conventional interpolation methods.