Deconvolution of images with periodic striping noise
Zuoguan Wang, Wujun Xu, Yutian Fu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
In this paper a new deconvolution algorithm is presented concerning images contaminated by periodic stripes. Inspired by the 2-D power spectrum distribution property of periodic stripes in the frequency domain, we construct a novel regularized inverse filter which allows the algorithm to suppress the amplification of striping noise in the Fourier inverse step and further get rid of most of them, and mirror-wavelet denoising is followed to remove the left colored noise. In simulations with striped images, this algorithm outperforms the traditional mirror-wavelet based deconvolution in terms of both visual effect and SNR comparison, only at the expense of slightly heavier computation load. The same idea about regularized inverse filter can also be used to improve other deconvolution algorithms, such as wavelet packets and wiener filters, when they are employed to images stained by periodic stripes.