Image Denoising and Zooming under the LMMSE Framework

1 Corresponding author. Email: [email protected]. This research is supported by the Hong Kong General Research Fund (PolyU 5330/07E) and the National Science Foundation Council of China under Grant no. 60634030. Abstract – Most of the existing image interpolation schemes assume that the image to be interpolated is noise free. This assumption is invalid in practice because noise will be inevitably introduced in the image acquisition process. Usually the image is denoised first and is then interpolated. The denoising process, however, may destroy the image edge structures and introduce artifacts. Meanwhile, edge preservation is a critical issue in both image denoising and interpolation. To address these problems, in this paper we propose a directional denoising scheme, which naturally endows a subsequent directional interpolator. The problems of denoising and interpolation are modeled as to estimate the noiseless and missing samples under the same framework of optimal estimation. The local statistics is adaptively calculated to guide the estimation process. For each noisy sample, we compute multiple estimates of it along different directions and then fuse those directional estimates for a more accurate output. The estimation parameters calculated in the denoising processing can be readily used to interpolate the missing samples. Compared with the conventional schemes that perform denoising and interpolation in tandem, the proposed noisy image interpolation method can reduce many noise-caused interpolation artifacts and preserve well the image edge structures.