A Parallel Approach To Train FLANN For An Adaptive Filter
Images get corrupted at the time of transmission due to various noises, such as additive white Gaussian noise, impulse noise or both of these two, salt and pepper noise, multiplicative noises, random value impulse noise and many more. Neural network based image lter is one of the most important example of adaptive image lter. Adaptive neural network lter remove various types of noise such as Gaussian noise and impulsive noise. Neural networks have already been applied in several domains of image processing including image ltering. But training of those neural networks consume much time before it is actually tested on such as image ltering. Applying parallelism to image processing is increasingly practical and necessary, as our desktops are becoming multi- core machines replacing single core. Therefore, this paper presents a parallel approach called image decomposition technique to train FLANN (Functional Link Articial Neural Network) before it is actually used for rectifying the corrupted pixels to restore the image. Experimental results obtained through SPMD (Single Program Multiple Data) simulation environment show that the proposed parallel approach to train the FLANN is feasible as it substantially reduces the training period and also make it an efcient lter to restore the image fairly well maintaining the quality of the ltered image.
Index Terms-FLANN salt and pepper SPMD Image decomposition
Nachiketa Tarasia Manoj Kumar Mishra Prakash Chandra Dash Sakti Kumar Samal
School of Computer Engineering KHT UNIVERSITY Bhubaneswar, Orissa, India MITM B.P.U.T Bhubaneswar, Orissa, India BRMIMIT, Pubasasan B.P.U.T Bhubaneswar, Orissa, India
国际会议
2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)
哈尔滨
英文
242-246
2011-01-18（万方平台首次上网日期，不代表论文的发表时间）