Penrose Demosaicking

Bai, Chenyan; Li, Jia; Lin, Zhouchen; Yu, Jian; Chen, Yen‐Wei

doi:10.1109/tip.2015.2409569

Cited by 18 publications

(6 citation statements)

References 68 publications

(109 reference statements)

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“…The weights t 4+k , k = 1, 2, 3, 4 of the mean are computed by using the green and the red channels. In particular, t 4+k = ϕ (τ 4+k ), where and e k , f k , p k and q k are as in (1). We argue analogously as in the computation of the weight t k , k = 1, 2, 3, 4, where the role of the blue channel is played by the red component.…”

Section: The Initialization Of the Green Channelsupporting

confidence: 53%

“…The weights t 1 , t 2 , t 3 , t 4 of the mean are computed by using the green and the blue channels. We define 1In particular, we get 2where k = 1, 2, 3, 4, ϕ is a suitable positive decreasing detection function and τ k is defined by and the pixels e k , f k , p k and q k are as in (1). When the differences between the green values on the pixels e k and f k (see the yellow arc in Fig. (2) for k = 1), and p k (see the cyan arc in Fig. (2) for k = 1), and between the blue values on the pixels (i, j)and q k (see the brown arc in Fig. (2) for k = 1) are small enough, then we can assume that there are no discontinuities between the pixels e k and (i, j) (see the red line in Fig.…”

Section: The Initialization Of the Green Channelmentioning

confidence: 99%

“…The weights t 8+k , k = 1, 2, 3, 4, of the mean are calculated by using the just initialized red channel and the observed blue channel. In particular, t 8+k is given by ϕ(τ 8+k ), k = 1, 2, 3, 4, where ϕ is the detection function used in initializing the green channel, and where e k , f k , p k and q k are as in (1). When the differences between the red values on the pixels e k and f k , e k and p k , and between the blue values on the pixels (i, j) and q k , are sufficiently small, then we can suppose that there are no edges between the pixels (i, j) and e k .…”

Section: The Initialization Of the Red Valuesmentioning

confidence: 99%

See 2 more Smart Citations

A Fast Algorithm for the Demosaicing Problem Concerning the Bayer Pattern

Boccuto¹,

Gerace²,

Giorgetti³

et al. 2019

TOSIGPJ

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Introduction: In this paper, we deal with the demosaicing problem when the Bayer pattern is used. We propose a fast heuristic algorithm, consisting of three parts. Methods: In the first one, we initialize the green channel by means of an edge-directed and weighted average technique. In the second part, the red and blue channels are updated, thanks to an equality constraint on the second derivatives. The third part consists of a constant-hue-based interpolation. Results: We show experimentally how the proposed algorithm gives in mean better reconstructions than more computationally expensive algorithms.

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Section: The Initialization Of the Green Channelsupporting

confidence: 53%

Section: The Initialization Of the Green Channelmentioning

confidence: 99%

Section: The Initialization Of the Red Valuesmentioning

confidence: 99%

See 1 more Smart Citation

A Fast Algorithm for the Demosaicing Problem Concerning the Bayer Pattern

Boccuto¹,

Gerace²,

Giorgetti³

et al. 2019

TOSIGPJ

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show abstract

“…41. We first generate a Penrose layout that has the same resolution as the square layout, which has been detailed in Ref.…”

Section: Simulation Of Penrose Spatially Varying Exposures Imagesmentioning

confidence: 99%

“…They argued that different light sensitivities are already implied by the Bayer CFA, which can be further magnified by using carefully selected photographic filters. 41 However, super-resolution trades off temporal resolution for spatial resolution, whereas demosaicking trades off spatial resolution for spectral resolution. 2(d)].…”

Section: Introductionmentioning

confidence: 99%

Penrose high-dynamic-range imaging

Bai

Lin

et al. 2016

J. Electron. Imaging

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High-dynamic-range (HDR) imaging is becoming increasingly popular and widespread. The most common multishot HDR approach, based on multiple low-dynamic-range images captured with different exposures, has difficulties in handling camera and object movements. The spatially varying exposures (SVE) technology provides a solution to overcome this limitation by obtaining multiple exposures of the scene in only one shot but suffers from a loss in spatial resolution of the captured image. While aperiodic assignment of exposures has been shown to be advantageous during reconstruction in alleviating resolution loss, almost all the existing imaging sensors use the square pixel layout, which is a periodic tiling of square pixels. We propose the Penrose pixel layout, using pixels in aperiodic rhombus Penrose tiling, for HDR imaging. With the SVE technology, Penrose pixel layout has both exposure and pixel aperiodicities. To investigate its performance, we have to reconstruct HDR images in square pixel layout from Penrose raw images with SVE. Since the two pixel layouts are different, the traditional HDR reconstruction methods are not applicable. We develop a reconstruction method for Penrose pixel layout using a Gaussian mixture model for regularization. Both quantitative and qualitative results show the superiority of Penrose pixel layout over square pixel layout.

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An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images

Boccuto,

Gerace,

Giorgetti

et al. 2024

J Math Imaging Vis

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This paper proposes an edge-preserving regularization technique to solve the color image demosaicing problem in the realistic case of noisy data. We enforce intra-channel local smoothness of the intensity (low-frequency components) and inter-channel local similarity of the depth of object borders and textures (high-frequency components). Discontinuities of both the low-frequency and high-frequency components are accounted for implicitly, i.e., through suitable functions of the proper derivatives. For the treatment of even the finest image details, derivatives of first, second, and third orders are considered. The solution to the demosaicing problem is defined as the minimizer of an energy function, accounting for all these constraints plus a data fidelity term. This non-convex energy is minimized via an iterative deterministic algorithm, applied to a family of approximating functions, each implicitly referring to geometrically consistent image edges. Our method is general because it does not refer to any specific color filter array. However, to allow quantitative comparisons with other published results, we tested it in the case of the Bayer CFA and on the Kodak 24-image dataset, the McMaster (IMAX) 18-image dataset, the Microsoft Demosaicing Canon 57-image dataset, and the Microsoft Demosaicing Panasonic 500-image dataset. The comparisons with some of the most recent demosaicing algorithms show the good performance of our method in both the noiseless and noisy cases.

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Penrose Demosaicking

Cited by 18 publications

References 68 publications

A Fast Algorithm for the Demosaicing Problem Concerning the Bayer Pattern

A Fast Algorithm for the Demosaicing Problem Concerning the Bayer Pattern

Penrose high-dynamic-range imaging

An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images

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