A Novel Extreme Learning Machine based Denoising Algorithm

We introduce a fast and effective algorithm extreme learning machine (ELM) and apply it to image denoising. GA-ELM algorithm we proposed uses genetic algorithm(GA) to decide weights and bias in the ELM. It has better global optimal characteristics than traditional optimal ELM algorithm. In this paper, we used GA-ELM to do image denosing researching work. Firstly, this paper uses training samples to train GA-ELM as the noise detector. Then, we utilize the well-trained GA-ELM to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-ELM. Experiment data shows that this algorithm has better performance than other denosing algorithm.

[1]  Banshidhar Majhi,et al.  ANN based Adaptive Thresholding for Impulse Detection , 2006, SPPRA.

[2]  Baharum Baharudin,et al.  Mixed Impulse Fuzzy Filter Based on MAD, ROAD, and Genetic Algorithms , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

[3]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[4]  Yrjö Neuvo,et al.  Detail-preserving median based filters in image processing , 1994, Pattern Recognit. Lett..

[5]  I.V. Apalkov,et al.  Neural Network Adaptive Switching Median Filter for Image Denoising , 2005, EUROCON 2005 - The International Conference on "Computer as a Tool".

[6]  Yue Yin,et al.  Mobility based energy efficient and multi-sink algorithms for consumer home networks , 2013, IEEE Transactions on Consumer Electronics.

[7]  Zhijie Wang,et al.  Temporal association based on dynamic depression synapses and chaotic neurons , 2011, Neurocomputing.

[8]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[9]  Lei Shu,et al.  A Distance-Based Energy Aware Routing Algorithm for Wireless Sensor Networks , 2010, Sensors.

[10]  Youxian Sun,et al.  An estimation of the domain of attraction for recurrent neural networks with time-varying delays , 2008, Neurocomputing.

[11]  Raymond H. Chan,et al.  A Detection Statistic for Random-Valued Impulse Noise , 2007, IEEE Transactions on Image Processing.

[12]  X. Cang,et al.  A New Filtering Algorithm Based on Extremum and Median Value , 2001 .

[13]  Hanqing Lu,et al.  A new extension of kernel feature and its application for visual recognition , 2008, Neurocomputing.

[14]  Raymond H. Chan,et al.  Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization , 2005, IEEE Transactions on Image Processing.

[15]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[16]  Michael L. Lightstone,et al.  A new efficient approach for the removal of impulse noise from highly corrupted images , 1996, IEEE Trans. Image Process..