DCT coefficients generation model for film grain noise and its application in super-resolution

Film grain noise (FGN) is generated by the procedure of capturing pictures using photographic film. Images with FGN are subjectively pleasing. However, FGN is difficult to compress and its pleasant features are difficult to preserve when the images are resized. So in literature, FGN is extracted first, then regenerated for the processed noise-free images. In this paper a new method is proposed to generate FGN. In contrast to some other models which generate FGN in spatial domain, our method captures the statistic feature of FGN in frequency domain. FGN is signal dependent and the signal independent scaled noise image (SNI) is obtained by scaling FGN by corresponding noise-free image. We model each discrete cosine transform (DCT) coefficient of SNI as a Gaussian random variable. The Gaussian model parameters can be estimated from the stack of all the blocks in SNI based on stationary assumption. Experimental results show that proposed model recovers FGN with similar visual and spectrum properties to the original FGN. We also apply proposed model in superresolution and the quality of resultant images are improved.