Circular spatial filtering under high-noise-variance conditions

This paper introduces a novel circular spatial filtering scheme for suppressing additive white Gaussian noise (AWGN) under high-noise-variance conditions. In this method, a circular spatial-domain window, whose weights are derived from two independent functions: (i) spatial distance and (ii) gray level distance, is employed for filtering. The proposed filter is different from the Bilateral filter [Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the IEEE internal conference on computer vision 1998. p. 836–46] and performs well under high-noise conditions. It is capable of smoothing Gaussian noise as well as retaining detailed information of images. It gives significant performance in terms of peak-signal-to-noise ratio (PSNR) and universal quality index (UQI) and outperforms many known existing spatial-domain and wavelet-domain filters. The filtered image also gives better visual quality than the existing methods.