Bayesian edge-preserving color image reconstruction from color filter array data

Modern digital cameras use specific arrangement of Color Filter Array to sample light wavelength corresponding to visible colors. The most common Color Filter Array is the Bayer filter that samples only one color per pixel. To recover the full resolution image, an interpolation algorithm can be used. This process is called demosaicing and it is one of the first processing stages of a digital imaging pipeline. We introduce a novel data-driven model for demosaicing that takes into account the different requirements for reconstruction of the image Luma and Chrominance channels. The final model is a parallel composition of two reconstruction networks with individual architecture and trained with distinct loss functions. In order to solve the overfitting problem, we prepared a dataset that contains groups of patches that share common chromatic and spectral characteristics. We reported the reconstruction error on noise-free images and measured the effect of random noise and quantization noise in the demosaicing reconstruction. To test our model performance, we implemented the network on NVIDIA Jetson Nano, obtaining an end-to-end running time of less than one second for a full frame 12 MPixel image.

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Compression of Bayer color filter array data

Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429) ◽

10.1109/icip.2003.1246665 ◽

2004 ◽

Cited By ~ 6

Author(s):

C.C. Koh ◽

S.K. Mitra

Keyword(s):

Color Filter ◽

Color Filter Array ◽

Array Data ◽

Filter Array

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The Effect of the Color Filter Array Layout Choice on State-of-the-Art Demosaicing

Sensors ◽

10.3390/s19143215 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3215 ◽

Cited By ~ 2

Author(s):

Ana Stojkovic ◽

Ivana Shopovska ◽

Hiep Luong ◽

Jan Aelterman ◽

Ljubomir Jovanov ◽

...

Keyword(s):

Color Image ◽

State Of The Art ◽

Image Data ◽

Linear Interpolation ◽

Color Filter ◽

Color Filter Array ◽

Full Color ◽

Design Choice ◽

Filter Array ◽

The One

Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development in the past 20 years ranges from simple linear interpolation to modern neural-network-based (NN) approaches that encode the prior knowledge of millions of training images to fill in missing data in an inconspicious way. CFA design, on the other hand, is less well studied, although still recognized to strongly impact demosaicing performance. This is because demosaicing algorithms are typically limited to one particular CFA pattern, impeding straightforward CFA comparison. This is starting to change with newer classes of demosaicing that may be considered generic or CFA-agnostic. In this study, by comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing. We test the hypothesis that, with the increasing power of NN-based demosaicing, the influence of optimal CFA design on system performance decreases. This hypothesis is supported with the experimental results. Such a finding would herald the possibility of relaxing CFA requirements, providing more freedom in the CFA design choice and producing high-quality cameras.

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