Structure-Oriented Multidirectional Wiener Filter for Denoising of Image and Video Signals

In this letter, we propose a structure-oriented multidirectional Wiener filter to reduce additive white Gaussian noise in image and video signals. A local activity profile based on second derivatives is used to restrict filtering to homogeneous directions to combat blurring. The proposed filter improves the Wiener estimate of denoised pixels to reduce the residual blurring of the conventional Wiener filter while achieving higher noise-reduction gains of up to 5.6 dB peak signal-to-noise-ratio (PSNR). The parameters of the proposed filter (block size, shape and coefficients) are adapted to image structure and noise level for optimization with respect to noise-reduction gain and structure preservation. The effectiveness of the proposed method is shown using both the PSNR and the modulation transfer function calculated for a range of spatial frequencies to measure the degradation in contrast due to blurring. Our results show that the proposed method achieves a higher contrast transfer ratio than the conventional Wiener filter indicating improved preservation of high frequency content. We also show the performance of the proposed filter relative to reference anisotropic diffusion and wavelet methods.

I. Introduction

Noise in image and video signals represents an unwanted phenomenon that significantly disturbs the performance of image and video processing algorithms. Noise-reduction filters are thus used to improve algorithms performance. The requirements on those filters are: 1) high noise-reduction gain; 2) structure and contrast preservation; and 3) low computational cost. In general, noise filters work better if they are adaptive to both signal content and noise level.