Bilateral filtering is an effective technique for reducing image noise while preserving edge content. The filter kernel is constructed based on two criteria of neighboring pixels, namely photometric resemblance and geometric proximity. The Euclidean distance is used as a metric for the photometric portion of the kernel in the classic definition of the filter. We illustrate in this paper a simplified method for calculating the Euclidean distance metric which reduces the computational complexity of the filter. Furthermore, we generalize the idea of bilateral filtering by linking the filter processing parameters to the noise profile of a CMOS image sensor and present a simple method for tuning the performance of the filter.
The classical bilateral filter smoothes images and preserves edges using a nonlinear combination of surrounding pixels. Our modified bilateral filter advances this approach by sharpening edges as well. This method uses geometrical and photometric distance to select pixels for combined low and high pass filtering. It also uses a simple window filter to reduce computational complexity.