Abstract
A mosaic of color filter arrays (CFAs) is commonly used in digital cameras as a spectrally selective filter to capture color images. The captured raw image is then processed by a demosaicing algorithm to recover the full-color image. In this paper, we formulate demosaicing as a restoration problem and solve it by minimizing the difference between the input raw image and the sampled full-color result. This under-constrained minimization is then solved with a novel convolutional neural network that estimates a linear subspace for the result at local image patches. In this way, the result in an image patch is determined by a few combination coefficients of the subspace bases, which makes the minimization problem tractable. This approach further allows joint learning of the CFA and demosaicing network. We demonstrate the superior performance of the proposed method by comparing it with state-of-the-art methods in both settings of noise-free and noisy data.
Highlights
In digital imaging pipeline, a mosaic of color filter arrays (CFAs) is applied in front of the camera sensor to capture the raw image where each pixel only measures the intensity of one color band
The demosaicing problem can be formulated as an image restoration problem, which aims to recover a full-color image I from the degraded raw image M = H I + N, where H is the degradation matrix and N is the additive noise
We propose a novel pattern sharing convolutional (PSC) layer to allow the network to deal with the sparse image M
Summary
A mosaic of color filter arrays (CFAs) is applied in front of the camera sensor to capture the raw image where each pixel only measures the intensity of one color band This raw image measurement is referred to as imaging process and typically processed by a demosaicing algorithm to reconstruct a full-color image. In contrast to other image restoration tasks, e.g., deblurring and denoising, where the matrices H are uncontrollable, the matrix H for demosaicing depends on the adopted CFA that is designable. Both the design of CFA and demosaicing algorithm are critical for the quality of the final color image and have been studied for decades
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