IMAGE-RESTORATION WITH MULTIPLICATIVE NOISE - INCORPORATING THE SENSOR NONLINEARITY

In this correspondence, we derive a linear minimum mean-square-error deconvolution filter in the presence of multiplicative noise. As for the motivation for the development, we discuss the importance of incorporating the nonlinear sensor characteristics into the restoration of noisy and blurred scanned photographic images. We propose restoring images in the "exposure domain" where a linear convolutional relationship between the original and the observed images can be established. However, the observation noise manifests itself as multiplicative noise in the exposure domain. We demonstrate noteworthy results in restoring photographic blurred images using the proposed filter in the exposure domain, whereas the use of the classical Wiener filter in the density domain, with additive noise assumption, does not yield any visible improvement.