Design of a Low-Power VLSI Macrocell for Nonlinear Adaptive Video Noise Reduction
- PDF / 861,366 Bytes
- 10 Pages / 600 x 792 pts Page_size
- 101 Downloads / 151 Views
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: [email protected]
Luca Fanucci Institute of Electronics, Information Engineering and Telecommunications, National Research Council, Via Caruso, 56122 Pisa, Italy Email: [email protected]
Pierangelo Terreni Department of Information Engineering, University of Pisa, Via Caruso, 56122 Pisa, Italy Email: [email protected] 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 3.8 dB with respect to a nonadaptive solution. The VLSI macrocell has been realized in a 0.18 µm 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 to model the noise in case of digital video broadcasting [3] or CCD/CMOS cameras [1, 2, 3] while impulsive-like noise usually affects images from satellite TV decoders [2, 3]. An impulsive noise model is also used for faulty bits during coding and transmission or for video scanned from damaged films. To remove meaningless noise information, while preserving fine image details, a large variety of nonlinear filtering methods [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] have been proposed in literature since conventional linear filters are known to blur the images. They typically involve weighted averaging masks in case of Gaussian noise or order-statistic filtering in case of impulsive one. In some cases both methods have been com-
bined to better withstand the different noise distributions in various video applications. For the real-time implementation of these techniques several solutions, based on dedicated applied specific integrated circuits (ASIC) technology or software realization for commercial digital signal processors (DSPs) have been proposed [2, 6, 10, 12, 13]. The above approaches are typically affected by two main drawb
Data Loading...
