163 research outputs found

    The effect of the color filter array layout choice on state-of-the-art demosaicing

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    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

    Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution

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    Incomplete color sampling, noise degradation, and limited resolution are the three key problems that are unavoidable in modern camera systems. Demosaicing (DM), denoising (DN), and super-resolution (SR) are core components in a digital image processing pipeline to overcome the three problems above, respectively. Although each of these problems has been studied actively, the mixture problem of DM, DN, and SR, which is a higher practical value, lacks enough attention. Such a mixture problem is usually solved by a sequential solution (applying each method independently in a fixed order: DM →\to DN →\to SR), or is simply tackled by an end-to-end network without enough analysis into interactions among tasks, resulting in an undesired performance drop in the final image quality. In this paper, we rethink the mixture problem from a holistic perspective and propose a new image processing pipeline: DN →\to SR →\to DM. Extensive experiments show that simply modifying the usual sequential solution by leveraging our proposed pipeline could enhance the image quality by a large margin. We further adopt the proposed pipeline into an end-to-end network, and present Trinity Enhancement Network (TENet). Quantitative and qualitative experiments demonstrate the superiority of our TENet to the state-of-the-art. Besides, we notice the literature lacks a full color sampled dataset. To this end, we contribute a new high-quality full color sampled real-world dataset, namely PixelShift200. Our experiments show the benefit of the proposed PixelShift200 dataset for raw image processing.Comment: Code is available at: https://github.com/guochengqian/TENe

    Spatio-Spectral Sampling and Color Filter Array Design

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    Owing to the growing ubiquity of digital image acquisition and display, several factors must be considered when developing systems to meet future color image processing needs, including improved quality, increased throughput, and greater cost-effectiveness. In consumer still-camera and video applications, color images are typically obtained via a spatial subsampling procedure implemented as a color filter array (CFA), a physical construction whereby only a single component of the color space is measured at each pixel location. Substantial work in both industry and academia has been dedicated to post-processing this acquired raw image data as part of the so-called image processing pipeline, including in particular the canonical demosaicking task of reconstructing a full-color image from the spatially subsampled and incomplete data acquired using a CFA. However, as we detail in this chapter, the inherent shortcomings of contemporary CFA designs mean that subsequent processing steps often yield diminishing returns in terms of image quality. For example, though distortion may be masked to some extent by motion blur and compression, the loss of image quality resulting from all but the most computationally expensive state-of-the-art methods is unambiguously apparent to the practiced eye. … As the CFA represents one of the first steps in the image acquisition pipeline, it largely determines the maximal resolution and computational efficiencies achievable by subsequent processing schemes. Here, we show that the attainable spatial resolution yielded by a particular choice of CFA is quantifiable and propose new CFA designs to maximize it. In contrast to the majority of the demosaicking literature, we explicitly consider the interplay between CFA design and properties of typical image data and its implications for spatial reconstruction quality. Formally, we pose the CFA design problem as simultaneously maximizing the allowable spatio-spectral support of luminance and chrominance channels, subject to a partitioning requirement in the Fourier representation of the sensor data. This classical aliasing-free condition preserves the integrity of the color image data and thereby guarantees exact reconstruction when demosaicking is implemented as demodulation (demultiplexing in frequency)

    Demosaicing of Color Images by Accurate Estimation of Luminance

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    Digital cameras acquire color images using a single sensor with Color filter Arrays. A single color component per pixel is acquired using color filter arrays and the remaining two components are obtained using demosaicing techniques. The conventional demosaicing techniques existent induce artifacts in resultant images effecting reconstruction quality. To overcome this drawback a frequency based demosaicing technique is proposed. The luminance and chrominance components extracted from the frequency domain of the image are interpolated to produce intermediate demosaiced images. A novel Neural Network Based Image Reconstruction Algorithm is applied to the intermediate demosaiced image to obtain resultant demosaiced images. The results presented in the paper prove the proposed demosaicing technique exhibits the best performance and is applicable to a wide variety of images
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