Proceedings ArticleDOI
A nonlinear digital filter using multi-layered neural networks
Kaoru Arakawa,H. Harashima +1 more
- pp 424-428
Reads0
Chats0
TLDR
A nonlinear digital filter utilizing multilayered neural networks is proposed, which significantly reduces random noises superimposed on signals which contain sharp edges, while preserving their sharpness.Abstract:
A nonlinear digital filter utilizing multilayered neural networks is proposed. This filter significantly reduces random noises superimposed on signals which contain sharp edges, while preserving their sharpness. In addition, degradation of the capability for noise reduction due to the increase of the noise power is greatly suppressed compared to previously proposed nonlinear filters. The high performance of this neural filter is demonstrated in computer simulations and actual image processing. When the noise power is small, the performance of the neural filter is almost the same as that of the epsilon -filter, which corresponds to a simplified neural filter; however, when the noise power is large, the effectiveness of the neural filter is clearly demonstrated. >read more
Citations
More filters
Journal ArticleDOI
Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography.
TL;DR: In this study, a pattern-recognition technique based on an artificial neural network (ANN), which is called a massive training artificial Neural network (MTANN), for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography (CT) images is investigated.
Journal ArticleDOI
Neural edge enhancer for supervised edge enhancement from noisy images
TL;DR: The NEE was robust against noise, was able to enhance continuous edges from noisy images, and was superior to the conventional edge enhancers in similarity to the desired edges.
Journal ArticleDOI
A Simple Neural Network Pruning Algorithm with Application to Filter Synthesis
TL;DR: By the experiment to synthesize the filter for solving real signal processing tasks, it has been shown that the NN obtained by the proposed method is superior to that obtaining by the conventional method in terms of the filter performance and the computational cost.
Journal ArticleDOI
Efficient approximation of neural filters for removing quantum noise from images
TL;DR: Efficient filters are presented that approximate neural filters (NFs) that are trained to remove quantum noise from images are sufficient for approximation of the trained NFs and efficient at computational cost.
Journal Article
Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences
TL;DR: The experimental results demonstrated that the performance on edge-preserving smoothing of the NFF, obtained by the proposed framework, is superior to that of the conventional neural and dynamic filters.
References
More filters
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI
ϵ‐separating nonlinear digital filter and its applications
TL;DR: In this paper, the basic ϵ-filter and its modifications to a trend-adaptive filter and a two-dimensional filter are described and the effectiveness of the new filter is demonstrated by computer simulation.
Proceedings Article
Optimization of a Piecewise Linear Digital Filter
Kaoru Arakawa,Hiroshi Harashima +1 more