Adaptive Image Filtering Using Run-Time Reconfiguration

by Srivastava, Nitin

This thesis implements an adaptive linear smoothing image filtering algorithm, on a Virtex-E FPGA using run-time reconfiguration (RTR). An adaptive filter uses a filtering window that runs over the entire image pixel-by-pixel, generating new (filtered) values of the pixels. As the name suggests, an adaptive filter can adapt to the varying nature of an image by adjusting the coefficients of the filtering window depending upon the local variance in the intensity values of pixels. It filters an image in a non-uniform fashion providing greater smoothing in largely uniform areas of the image and lesser smoothing when it encounters edges and step changes in the image. These continual changes, in the coefficient values of the adaptive filter pose a problem in utilizing run-time reconfiguration (RTR) for its implementation, as benefits of RTR emerge only with considerable computing time between reconfigurations. This thesis provides a solution to this problem and reduces the running time of the algorithm through aggressive use of RTR. This work provides details on the RTR implementation of an adaptive filter, along with an estimate of running time and hardware resource requirements, when synthesized on the Virtex-E FPGA. We use a 3 ×3 size filtering window, and a 256 256 ×size gray scale image as a specific case, achieving speedup of 31 and 84 over pure software implementations running on Pentium III and Sun Ultra systems respectively.