Design and real-time implementation of a low-cost noise reduction system for video applications

ASIP Design and Synthesis for Non Linear Filtering in Image Processing

“…This number varies slightly with image size and aspect ratio. The ASIP performs well comparing the synthesis results with stateof-art implementations of similar non linear video filtering algorithms on DSP [15] or dedicated VLSI cells [16]. DSP-based implementations have been proposed in the literature for the real time elaboration of up to CIF videos but their power cost is in the order of watts, more than one order of magnitude higher than the ASIP power consumption.…”

Design of a Low-Power VLSI Macrocell for Nonlinear Adaptive Video Noise Reduction

“…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.…”

“…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.…”

“…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 drawbacks.…”

“…Secondly, the filters are not optimized for low-power consumption which is mandatory for the success of any battery-powered video application such as wireless cameras, 3G mobile phones, and personal digital assistants to name but a few. Conventional DSP implementations of noise smoothing filters (e.g., using a Texas Instruments C80 [6,13] or a Philips Trimedia1000 [2]) require thousands of mW while the power budget for system-on-chip (SoC) video communications terminals (implementing a video codec plus pre/postfiltering units) is often bounded to few hundreds of mW [14,15]. Reducing power consumption is also a key issue in highly integrated SoC to avoid heat removal problems requiring the use of costly packaging and cooling mechanisms.…”