A CSF-based preprocessing method for image deblurring

This paper aims at increasing the visual quality of a blurred image according to the contrast sensitivity of a human observer. The main idea is to enhance those image details which can be perceived by a human observer without introducing annoying visible artifacts. To this aim, an adaptive wavelet decomposition is applied to the original blurry image. This decomposition splits the frequency axis into subbands whose central frequency and amplitude width are built according to the contrast sensitivity. The details coefficients of that decomposition are then properly modified according to the just noticeable contrast at each frequency band. Preliminary experimental results show that the proposed method increases the visual quality of the blurred image without introducing visible artifacts. In addition, the contrast sensitivity-based image is a good and recommended initial guess for iterative deblurring methods since it allows them to significantly reduce ringing artifacts and halo effects in the final image.