Image Processing Reference

In-Depth Information

The variance
σ
2
(
x
) is approximated as a piecewise linear function depending on the signal

x
, with the slope
m
(
x
) and the intercept
t
(
x
) based on the measurement data in
Figure 1
(a).

Because of the dual-gain read-out the values for
m
(
x
) and
t
(
x
) are piecewise constant.

Based on the model for the camera noise in the raw data, we describe in the next section the

shape-adaptive DCT denoising method and how to integrate the noise model in the SA-DCT.

3 Adaptive raw data denoising

Our goal is to find an algorithm, which provides a high visual quality of the denoising results,

and which is additionally efficient in terms of hardware implementation costs. Regarding

the visual quality, a common problem of denoising algorithms is blurring of edges or ine

details in the image (oversmoothing). The shape-adaptive denoising algorithm [
7
] prevents

from oversmoothing by using a homogenous neighborhood for denoising. As proposed by Foi

we use the local polynomial approximation and intersection of confidence interval technique

(LPA-ICI) to find an adequate neighborhood for each pixel.

directly be used on Bayer data, because the neighboring pixels do not have the same color due

to the Bayer mask.

To find a way of estimating the neighborhood based on the Bayer data, we apply a lumin-

ance transformation. In color image denoising, a color space transformation from RGB to a

luminance-chrominance color space (e.g. YCbCr) is usual. As the structural information in nat-

ural images is mostly contained in the luminance data, it is effective to perform the neighbor-

hood estimation on the luminance channel only and use this neighborhood for denoising all

three channels. In our case, we apply a similar strategy; we obtain an estimation of the lumin-

ance channel based on the Bayer data. We discuss this luminance transformation in the next

section.

3.1 Luminance Transformation of Bayer Data

To find a luminance estimation based on the Bayer data we tested different techniques: filter

ing with a fixed filter kernel, partial debayering, and a new method we call “virtual lumin-

ance.” Partial debayering means, we take the debayered green channel as luminance estima-

tion directly, as the green channel is most dominant for the luminance. We used the camera

debayering method (ADA), which can be applied by downloading the free “ARRIRAW Con-

Bayer data. We used two different filters: a Gaussian filter kernel and a filter similar to the lu-

Bayer data by using the neighboring color values, which we call “virtual,” because the result

gives us luminance values which are located between the pixels.

The results on our test image “city” in
Table 1
show that the difference in terms of peak

signal-to-noise ratio (PSNR) of the denoising result is marginal. The best value is reached by

the camera debayering and Gaussian filtering. We use the Gaussian filtering for our method,

as it shows one of the best results and additionally is a very simple and cost-efficient method.

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