Abstract
This paper presents a Wiener-filter-based image denoising technique with improved performance that employs a new power spectrum estimation method. The frequency domain Wiener filter is implemented and compared with the Wiener filter in other domains, such as the spatial and wavelet domains. It is also compared with state-of-the-art filtering approaches. The proposed power spectrum estimation method thresholds the power spectrum of a noisy image into two regions: low- and high-frequency regions. The image power spectrum is obtained from the low-frequency region because image components are generally concentrated at low frequencies. The noise power spectrum is obtained from the high-frequency region because noise is assumed to be concentrated at high frequencies. To remove noise more accurately, the noise power spectrum is also estimated from the low-frequency region because white noise exists with a flat power spectrum distribution throughout the noisy image. Simulation experiments show that the frequency domain Wiener filter with the proposed power spectrum estimation method preserves an image's fine textures and edges effectively. The filter is found to be lower or equivalent in computational complexity when compared with the conventional approaches.
