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Image Demosaicing: A Systematic Survey
"... Image demosaicing is a problem of interpolating full-resolution color images from so-called color-filter-array (CFA) samples. Among various CFA patterns, Bayer pattern has been the most popular choice and demosaicing of Bayer pattern has attracted renewed interest in recent years partially due to th ..."
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Cited by 39 (1 self)
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Image demosaicing is a problem of interpolating full-resolution color images from so-called color-filter-array (CFA) samples. Among various CFA patterns, Bayer pattern has been the most popular choice and demosaicing of Bayer pattern has attracted renewed interest in recent years partially due to the increased availability of source codes/executables in response to the principle of “reproducible research”. In this article, we provide a systematic survey of over seventy published works in this field since 1999 (complementary to previous reviews 22, 67). Our review attempts to address important issues to demosaicing and identify fundamental differences among competing approaches. Our findings suggest most existing works belong to the class of sequential demosaicing- i.e., luminance channel is interpolated first and then chrominance channels are reconstructed based on recovered luminance information. We report our comparative study results with a collection of eleven competing algorithms whose source codes or executables are provided by the authors. Our comparison is performed on two data sets: Kodak PhotoCD (popular choice) and IMAX high-quality images (more challenging). While most existing demosaicing algorithms achieve good performance on the Kodak data set, their performance on the IMAX one (images with varying-hue and high-saturation edges) degrades significantly. Such observation suggests the importance of properly addressing the issue of mismatch between assumed model and observation data in demosaicing, which calls for further investigation on issues such as model validation, test data selection and performance evaluation.
Adaptive Filtering for Color Filter Array Demosaicking
"... Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the ..."
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Cited by 13 (0 self)
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Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: 1) the high frequencies are similar across three color components, and 2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image, and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images as well as raw CFA data verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost.
An empirical Bayes EM-wavelet unification for simultaneous denoising, interpolation, and/or demosaicing
- In Image Processing, 2006 IEEE International Conference on. IEEE
, 2006
"... We present a unified framework for coupling the EM algorithm with the Bayesian hierarchical modeling of neighboring wavelet coeffi-cients of image signals. Within this framework, problems with miss-ing pixels or pixel components, and hence unobservable wavelet co-efficients, are handled simultaneous ..."
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Cited by 10 (8 self)
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We present a unified framework for coupling the EM algorithm with the Bayesian hierarchical modeling of neighboring wavelet coeffi-cients of image signals. Within this framework, problems with miss-ing pixels or pixel components, and hence unobservable wavelet co-efficients, are handled simultaneously with denoising. The hyper-parameters of the model are estimated via the marginal likelihood by the EM algorithm, and a part of the output of its E-step auto-matically provide optimal estimates, given the specified Bayesian model, of the noise-free image. This unified empirical-Bayes based framework, therefore, offers a statistically principled and extremely flexible approach to a wide range of pixel estimation problems in-cluding image denoising, image interpolation, demosaicing, or any combinations of them. Index Terms — wavelets, missing data, denoising, interpolation 1.
Practical Implementation of LMMSE Demosaicing Using Luminance and Chrominance Spaces
, 2006
"... Most digital color cameras sample only one color at each spatial location, using a single sensor coupled with a color filter array (CFA). An interpolation step called demosaicing (or demosaicking) is required for rendering a color image from the acquired CFA image. Already proposed Linear Minimum Me ..."
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Cited by 7 (1 self)
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Most digital color cameras sample only one color at each spatial location, using a single sensor coupled with a color filter array (CFA). An interpolation step called demosaicing (or demosaicking) is required for rendering a color image from the acquired CFA image. Already proposed Linear Minimum Mean Square Error (LMMSE) demosaicing provides a good tradeoff between quality and computational cost for embedded systems. In this paper we propose a modification of the stacked notation of superpixels, which allows an effective computing of the LMMSE solution from an image database. Moreover, this formalism is used to decompose the CFA sampling into a sum of a luminance estimator and a chrominance projector. This decomposition allows interpreting estimated filters in term of their spatial and chromatic properties and results in a solution with lower computational complexity than other LMMSE approaches for the same quality.
A new color filter array with optimal properties for noiseless and noisy color image acquisition
- IEEE Trans. ImageProcess.,vol.20
, 2011
"... Abstract — Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market, but there has been recently a renewed interest for the design of CFAs [2]–[6]. However, robustness to noise is often neglected in t ..."
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Cited by 3 (1 self)
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Abstract — Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market, but there has been recently a renewed interest for the design of CFAs [2]–[6]. However, robustness to noise is often neglected in the design, though it is crucial in practice. In this work, we present a new 2 × 3-periodic CFA which provides, by construction, the optimal tradeoff between robustness to aliasing, chrominance noise and luminance noise. Moreover, a simple and efficient linear demosaicking algorithm is described, which fully exploits the spectral properties of the CFA. Practical experiments confirm the superiority of our design, both in noiseless and noisy scenarios. Index Terms — Color filter array (CFA), color imaging, de-mosaicking, digital camera pipeline, spatio-spectral sampling, luminance/chrominance basis, noise sensitivity. I.
Random patterns for color filter arrays with good spectral properties
- IEEE Trans. Image Proc., submitted
"... Abstract—Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA is the most popular, but the design of alternative CFAs has actually received very few attention. We propose in this article new CFAs having random patterns; that i ..."
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Abstract—Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA is the most popular, but the design of alternative CFAs has actually received very few attention. We propose in this article new CFAs having random patterns; that is, their filters are arranged non-periodically. We base this design on a theoretical analysis of the spectral characteristics of CFAs, showing that the chrominance should be modulated at the highest possible frequency. Hence, random patterns with blue noise properties are particularly adequate for color sampling. Our new CFAs yield demosaicked images where the artifacts appear as incoherent noise, which is less visually disturbing than the moire ́ structures that appear with periodic CFAs. Thus, this short study aims at pointing out the potential benefits of using random CFAs and the need for new generic demosaicking algorithms that would be able to exploit at best their properties. Index Terms—Color filter array, random pattern, blue noise, dart throwing, demosaicking I.
Signal-Dependent Noise Characterization in Haar Filterbank Representation
"... Owing to the properties of joint time-frequency analysis that compress energy and approximately decorrelate temporal redundancies in sequential data, filterbank and wavelets are popular and convenient platforms for statistical signal modeling. Motivated by the prior knowledge and empirical studies, ..."
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Owing to the properties of joint time-frequency analysis that compress energy and approximately decorrelate temporal redundancies in sequential data, filterbank and wavelets are popular and convenient platforms for statistical signal modeling. Motivated by the prior knowledge and empirical studies, much of the emphasis in signal processing has been placed on the choice of the prior distribution for these transform coefficients. In this paradigm however, the issues pertaining to the loss of information due to measurement noise are difficult to reconcile because the effects of point-wise signal-dependent noise permeate across scale and through multiple coefficients. In this work, we show how a general class of signal-dependent noise can be characterized to an arbitrary precision in a Haar filterbank representation, and the corresponding maximum a posteriori estimate for the underlying signal is developed. Moreover, the structure of noise in the transform domain admits a variant of Stein’s unbiased estimate of risk conducive to processing the corrupted signal in the transform domain. We discuss estimators involving Poisson process, a situation that arises often in real-world applications such as communication, signal processing, and imaging. 1.
A new class of color filter arrays with optimal sensing properties,” Research Report HAL-00347433
, 2008
"... Abstract — Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market and little attention has been directed to the design of alternative CFAs in the literature. Recent works of Hirakawa et al. [1]–[4] ..."
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Cited by 1 (0 self)
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Abstract — Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market and little attention has been directed to the design of alternative CFAs in the literature. Recent works of Hirakawa et al. [1]–[4] introduced new insights in this field by focusing on the spectral properties of CFAs and operating directly in the Fourier domain. However, this new paradigm is generic and leaves open questions about the optimization of the many available parameters. In this work, we investigate the link between them and the light sensitivity and color discrimination capabilities of the CFA. Indeed, these characteristics determine the quality of the whole imaging pipeline, since they directly control its sensitivity to noise. By optimizing the key parameters, we obtain a class of new CFAs with optimal properties, in which the shortest 2×3 pattern shows up as the best compromise. Moreover, a simple and efficient linear demosaicking algorithm is associated to these CFAs, which fully exploits their spectral properties. Practical experiments confirm the superiority of our new design. Index Terms — Color filter array (CFA), color imaging, demosaicking, digital camera pipeline, spatio-spectral sampling, luminance/chrominance gains, noise sensitivity. I.
A Generic Variational Framework for Demosaicking and Performance Analysis of Color Filter Arrays
, 2009
"... Abstract — We propose a novel method for image demosaicking from samples obtained with a completely arbitrary color filter array (CFA). We adopt a variational approach where the reconstructed image has maximal smoothness under the constraint of consistency with the available measurements. This optim ..."
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Abstract — We propose a novel method for image demosaicking from samples obtained with a completely arbitrary color filter array (CFA). We adopt a variational approach where the reconstructed image has maximal smoothness under the constraint of consistency with the available measurements. This optimization problem boils down to a large, sparse system of linear equations to solve, for which we propose an iterative algorithm. Although the proposed approach is linear, it yields visually pleasing demosaicked images and provides a robust framework for comparing the performances of CFAs. Index Terms — Demosaicking, color filter array, variational reconstruction, regularized inverse problem I.
AN EMPIRICAL BAYES EM-WAVELET UNIFICATION FOR SIMULTANEOUS DENOISING, INTERPOLATION, AND/OR DEMOSAICING
"... We present a unified framework for coupling the EM algorithm with the Bayesian hierarchical modeling of neighboring wavelet coefficients of image signals. Within this framework, problems with missing pixels or pixel components, and hence unobservable wavelet coefficients, are handled simultaneously ..."
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We present a unified framework for coupling the EM algorithm with the Bayesian hierarchical modeling of neighboring wavelet coefficients of image signals. Within this framework, problems with missing pixels or pixel components, and hence unobservable wavelet coefficients, are handled simultaneously with denoising. The hyperparameters of the model are estimated via the marginal likelihood by the EM algorithm, and a part of the output of its E-step automatically provide optimal estimates, given the specified Bayesian model, of the noise-free image. This unified empirical-Bayes based framework, therefore, offers a statistically principled and extremely flexible approach to a wide range of pixel estimation problems including image denoising, image interpolation, demosaicing, or any combinations of them. Index Terms — wavelets, missing data, denoising, interpolation 1.
