Denoising camera data - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 1

CHAPTER 1

Denoising camera data

Shape-adaptive noise reduction for color filter

array image data

Tamara Seybold 1 ; Bernd Klässner 2 ; Walter Stechele 2

1 Arnold & Richter Cine Technik, München, Germany

2 Technische Universität München, München, Germany

Abstract
While denoising readily processed images has been studied extensively, the reduction of camera noise in
the camera raw data is still a challenging problem. Camera noise is signal-dependent and the raw data is
a color filter array (CFA) image, which means the neighboring values are not of the same color and stand-
ard denoising methods cannot be used. In this paper, we propose a new method for efficient raw data
denoising that is based on a shape-adaptive DCT (SA-DCT), which was originally proposed for non-CFA
data. Our method consists of three steps: a luminance transformation of the Bayer data, determining an
adequate neighborhood for denoising and hard thresholding in the SA-DCT domain. The SA-DCT is
applied on realistic CFA data and accounts for the signal-dependent noise characteristic using a locally
adaptive threshold and signal-dependent weights. We additionally present a computationally efficient
solution to suppress flickering in video data. We evaluate the method quantitatively and visually using
both realistically simulated test sequences and real camera data. Our method is compared to the state-
of-the-art methods and achieves similar performance in terms of PSNR. In terms of visual quality, our
method can reach more pleasant results compared to state-of-the-art methods, while the computational
complexity is kept low.

Keywords

Color denoising
Camera raw data
Color filter array
CFA data
Implementation cost
Video denoising