An Algorithm for Adaptive Mean Filtering and Its Hardware Implementation

Abstract

Noise due to the sensor and the electronics of a camera is an undesirable issue in any machine vision application. Such noise tends to corrupt images and to obstruct any further analysis. An algorithm to detect and cancel such noise, using statistical methods, is presented in this paper. The proposed algorithm is an adaptive mean filter, which filters out image regions that are found to be noise corrupted. The efficiency of the proposed filter was examined both qualitatively and quantitatively, by software simulation in several noisy conditions. The main advantage of the filter in hand is that it is appropriate for hardware implementation and can be easily incorporated to smart cameras. The hardware implementation of the filter is also presented in this paper. This implementation aims at time critical applications such as machine vision, inspection and visual surveillance.

Real-Time Stereo Vision Applications

“…As the working windows move over the image, overlapping pixels exist between adjacent windows. The serpentine memory architecture is used to temporarily store overlapping pixels in order to reduce the clock cycles needed to load image pixels into the module (Gasteratos et al, 2006). CA filtering design is most efficient when implemented in hardware, due to the highly parallel independent processing.…”