Biomedical subjects

Hong Ren Wu

Publications and source records attributed to Hong Ren Wu.

4 recordsLinked to original sources

PubMed2006

Partition-based vector filtering technique for suppression of noise in digital color images.

A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity.

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PubMed2006

Perceptually lossless medical image coding.

A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts has shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder.

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PubMed2006

A new adaptive backpropagation algorithm based on Lyapunov stability theory for neural networks.

A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks is developed in this paper. It is shown that the candidate of a Lyapunov function V(k) of the tracking error between the output of a neural network and the desired reference signal is chosen first, and the weights of the neural network are then updated, from the output layer to the input layer, in the sense that deltaV(k) = V(k) - V(k - 1) < 0. The output tracking error can then asymptotically converge to zero according to Lyapunov stability theory. Unlike gradient-based BP training algorithms, the new Lyapunov adaptive BP algorithm in this paper is not used for searching the global minimum point along the cost-function surface in the weight space, but it is aimed at constructing an energy surface with a single global minimum point through the adaptive adjustment of the weights as the time goes to infinity. Although a neural network may have bounded input disturbances, the effects of the disturbances can be eliminated, and asymptotic error convergence can be obtained. The new Lyapunov adaptive BP algorithm is then applied to the design of an adaptive filter in the simulation example to show the fast error convergence and strong robustness with respect to large bounded input disturbances.

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PubMed2005

A robust structure-adaptive hybrid vector filter for color image restoration.

A robust structure-adaptive hybrid vector filter is proposed for digital color image restoration in this paper. At each pixel location, the image vector (i.e., pixel) is first classified into several different signal activity categories by applying a modified quadtree decomposition to luminance component (image) of the input color image. A weight-adaptive vector filtering operation with an optimal window is then activated to achieve the best tradeoff between noise suppression and detail preservation. Through extensive simulation experiments conducted using a wide range of test color images, the filter has demonstrated superior performance to that of a number of well known benchmark techniques, in terms of both standard objective measurements and perceived image quality, in suppressing several distinct types of noise commonly considered in color image restoration, including Gaussian noise, impulse noise, and mixed noise.

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