Evaluation of Noise Characteristics
in Image Pipeline

Annabel Huo, Hyung Suk Kim, Sung Hee Park
March 21, 2009
Psych 221 : Applied Vision and Image Systems

  |   Introduction   |   Methods   |   Results   |   Conclusions   |   References   |   Appendix   |  

Introduction
    Color imaging sensors used in digital cameras acquire three spatially subsampled color channels with a color filter array (CFA) mosaic. The acquired image is then processed through an imaging pipeline of demosaicing, denoising and color correction. The algorithms used in each stage of the image pipeline are complex, several of them nonlinear and the effect on the noise is complicated.
    There are two goals in this project. First, this project investigates the effects of the order of demosaic and denoise process on images and image noise. The study focuses on commonly adopted denoising algorithms: BM3D, bilateral filtering and BLS_GSM; and four demosaic algorithms, namely bilinear interpolation, adaptive homogeneity, POCS and adaptive frequency domain method. Images and noises are monitored and analyzed in each stage of the pipeline, to understand how each stage/algorithm affects and manipulates the noise characteristics. Noise characteristics are evaluated by various metrics from MSE and sCIELAB to visual representations of spatial and color channel correlation of noise. Ultimately, this project will suggest a preferred order of the image pipeline.
   Since most of the demosaicing algorithms do not take the effects of denoising into account and vice versa, optimizing both stages is difficult, hence a joint demosaicing and denoising algorithm was proposed by Keigo Hirakawa et al. which combines these two procedures systematically into a single operation. In this project, we will also compare the performance of the joint demosaicing and denoising algorithm, by comparing its final images with the images generated by separate demosaic and denoise processes.