Adaptive demosaicking

Ramanath, Rajeev

doi:10.1117/1.1606459

Cited by 84 publications

(54 citation statements)

References 13 publications

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“…The authors verified that performing demosaicing and denoising jointly is more effective than treating them independently (Hirakawa and Parks, 2005b). Ramanath and Snyder (2003) proposed a bilateral filtering-based scheme to denoise, sharpen, and demosaic the image simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Spatially adaptive color filter array interpolation for noiseless and noisy data

Paliy

Katkovnik

Bilcu

et al. 2007

Int J Imaging Syst Tech

101

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Conventional single-chip digital cameras use color filter arrays (CFA) to sample different spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, to produce an RGB image. In this article, we propose a novel demosaicing algorithm for the Bayer CFA. For the algorithm design, we assume that, following the concept proposed in (Zhang and Wu, IEEE Trans Image Process 14 (2005), 2167-2178), the initial interpolation estimates of color channels contain two additive components: the true values of color intensities and the errors that are considered as an additive noise. A specially designed signaladaptive filter is used to remove this so-called demosaicing noise. This filter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of confidence intervals applied to select varying scales of LPA. This technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The presented CFA interpolation (CFAI) technique takes significant advantage from assuming that the original data is noise-free. Nevertheless, in many applications, the observed data is noisy, where the noise is treated as an important intrinsic degradation of the data. We develop an adaptation of the proposed CFAI for noisy data, integrating the denoising and CFAI into a single procedure. It is assumed that the data is given according to the Bayer pattern and corrupted by signal-dependant noise common for charge-coupled device and complementary-symmetry/metal-oxide semiconductor sensors. The efficiency of the proposed approach is demonstrated by experimental results with simulated and real data.

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Section: Introductionmentioning

confidence: 99%

Spatially adaptive color filter array interpolation for noiseless and noisy data

Paliy

Katkovnik

Bilcu

et al. 2007

Int J Imaging Syst Tech

101

View full text Add to dashboard Cite

show abstract

“…In (Lu & Tan, 2003) and in the first step of (Kimmel, 1999), each direction contributes to the interpolation with a weight which is proportional to the gradient inverse. In (Ramanath, 2003), the weights depend on the similarity to the center pixel after a first demosaicking step. Additionally, the framework to estimate R and B channels is improved in (Lu & Tan, 2003).…”

Section: Overviewmentioning

confidence: 99%

Comparison of Demosaicking Methods for Color Information Extraction

Faille¹

2005

Cutting Edge Robotics

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“…The full color image is reconstructed by interpolating the missing color samples. Color demosaicking has been extensively studied in spatial domain for still digital cameras [1], [2], [5]- [13], [15]- [21], [24], [25], [27], [28]. We call this class of color demosaicking techniques the intra-frame or spatial demosaicking.…”

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confidence: 99%

Temporal color video demosaicking via motion estimation and data fusion

Zhang

2006

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Color demosaicking of charge-coupled device (CCD) data has been thoroughly studied for single-sensor still digital cameras. However, there has seemingly been little research on color demosaicking techniques for single-sensor video digital cameras. The temporal dimension of a color mosaic image sequence can reveal new information on the missing color components due to the mosaic subsampling, which is otherwise unavailable in the spatial domain of individual frames. This paper proposes a temporal approach to color demosaicking. A pixel of the current frame is matched to another in a reference frame via motion analysis, such that the CCD sensor samples different color components of the same object position in the two frames. The resulting inter-frame estimates of missing color components are fused with suitable intra-frame estimates to achieve a more robust color restoration. Our experimental results demonstrate clear advantages of the presented temporal color demosaicking approach over its intra-frame counterparts in reducing the color artifacts.Index Terms-Bayer pattern, color demosaicking, data fusion, motion estimation, single-sensor digital video cameras.

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Adaptive demosaicking

Cited by 84 publications

References 13 publications

Spatially adaptive color filter array interpolation for noiseless and noisy data

Spatially adaptive color filter array interpolation for noiseless and noisy data

Comparison of Demosaicking Methods for Color Information Extraction

Temporal color video demosaicking via motion estimation and data fusion

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