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: Design of a Low-Power VLSI Macrocell for Nonlinear Adaptive Video Noise Reduction | EURASIP Journal on Applied Signal Processing 2004 12 1921-1930 2004 Hindawi Publishing Corporation Design of a Low-Power VLSI Macrocell for Nonlinear Adaptive Video Noise Reduction Sergio Saponara Department of Information Engineering University of Pisa Via Caruso 56122 Pisa Italy Email Luca Fanucci Institute of Electronics Information Engineering and Telecommunications National Research Council Via Caruso 56122 Pisa Italy Email Pierangelo Terreni Department of Information Engineering University of Pisa Via Caruso 56122 Pisa Italy Email Received 26 August 2003 Revised 19 February 2004 A VLSI macrocell for edge-preserving video noise reduction is proposed in the paper. It is based on a nonlinear rational filter enhanced by a noise estimator for blind and dynamic adaptation of the filtering parameters to the input signal statistics. The VLSI filter features a modular architecture allowing the extension of both mask size and filtering directions. Both spatial and spatiotemporal algorithms are supported. Simulation results with monochrome test videos prove its efficiency for many noise distributions with PSNR improvements up to dB with respect to a nonadaptive solution. The VLSI macrocell has been realized in a pm CMOS technology using a standard-cells library it allows for real-time processing of main video formats up to 30 fps frames per second 4CIF with a power consumption in the order of few mW. Keywords and phrases nonlinear image processing video noise reduction adaptive filters very large scale integration architectures low-power design. 1. INTRODUCTION Noise reduction is a key issue in any video system to improve the visual appearance of the images. Especially in consumer electronics the sources of images such as video recorders video cameras satellite decoders and so on are affected by different kinds of noise 1 2 3 . White Gaussian distribution is usually adopted .
