Frequency-domain Regularized Deconvolution for Images with Stripe Noise

This paper presents a new approach to the deconvolution for images contaminated by stripe noise. Inspired by the 2D power spectrum distribution property of stripe noise in the frequency domain, we construct a novel regularized inverse filter which allows the algorithm to suppress the amplification of stripe noise in the Fourier inverse step and further get rid of most of them, and a 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 stripe noise.