Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Training Methods for Image Noise Level Estimation on Wavelet Components | EURASIP Journal on Applied Signal Processing 2004 16 2400-2407 2004 Hindawi Publishing Corporation Training Methods for Image Noise Level Estimation on Wavelet Components A. De Stefano Institute of Sound and Vibration Research University of Southampton Highfield Hants SO17 1BJ UK Email ads@ P. R. White Institute of Sound and Vibration Research University of Southampton Highfield Hants SO17 1BJ UK Email prw@ W. B. Collis The Foundry 35-36 Great Marlborough Street London W1F 7JE UK Email bill@ Received 25 July 2003 Revised 14 January 2004 The estimation of the standard deviation of noise contaminating an image is a fundamental step in wavelet-based noise reduction techniques. The method widely used is based on the mean absolute deviation MAD . This model-based method assumes specific characteristics of the noise-contaminated image component. Three novel and alternative methods for estimating the noise standard deviation are proposed in this work and compared with the MAD method. Two of these methods rely on a preliminary training stage in order to extract parameters which are then used in the application stage. The sets used for training and testing 13 and 5 images respectively are fully disjoint. The third method assumes specific statistical distributions for image and noise components. Results showed the prevalence of the training-based methods for the images and the range of noise levels considered. Keywords and phrases noise estimation training methods wavelet transform image processing. 1. INTRODUCTION Noise reduction plays a fundamental role in image processing and wavelet analysis has been demonstrated to be a powerful method for performing image noise reduction 1 2 3 4 5 6 7 8 9 10 11 12 . The procedure for noise reduction is applied on the wavelet coefficients achieved using the wavelet decomposition and representing the image at different scales. After noise reduction the image is reconstructed using the .
