Abstract
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.
Highlights
Since Bayer’s original patent [1], (Bayer) Color Filter Array (CFA) demosaicing has established itself as the de facto standard method of acquiring multi-dimensional color images
By comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing
The baseline neural-network-based algorithm in this work is CDMNet [25]. We use this algorithm because of its high-quality performance and reconstruction power among the algorithms that belong to the group of modern learning-based demosaicing techniques and because our experiments show that it outperforms the state-of-the-art algorithms that belong to the group of classical demosaicing
Summary
Since Bayer’s original patent [1], (Bayer) Color Filter Array (CFA) demosaicing has established itself as the de facto standard method of acquiring multi-dimensional color images. General demosaicing in this sense would be defined as the reconstruction of a (multi-dimensional) color signal from an inherently single-dimensional array of (e.g., Charge-Coupled Device (CCD) or Complementary. A demosaicing algorithm reconstructs this into a (three-dimensional) full color signal. An optimal demosaicing system design would be constituted of the creation of an optimal interleaving pattern (the color filter array or CFA) and an optimal demosaicing algorithm that achieves the highest color reconstruction quality
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