A Low-Cost Stochastic Computing-based Fuzzy Filtering for Image Noise Reduction

Images are often corrupted with noise. As a result, noise reduction is an important task in image processing. Common noise reduction techniques, such as mean or median filtering, lead to blurring of the edges in the image, while fuzzy filters are able to preserve the edge information. In this work, we implement an efficient hardware design for a well-known fuzzy noise reduction filter based on stochastic computing. The filter consists of two main stages: edge detection and fuzzy smoothing. The fuzzy difference, which is encoded as bit-streams, is used to detect edges. Then, fuzzy smoothing is done to average the pixel value based on eight directions. Our experimental results show a significant reduction in the hardware area and power consumption compared to the conventional binary implementation while preserving the quality of the results.