Linear universal demosaicking of regular pattern color filter arrays

Singh, Mritunjay Kumar; Singh, Tripurari

doi:10.1109/icassp.2012.6288122

Cited by 6 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make the method perform better, it employs some nonlinear image enhancement methods, but the improvement seems to be limited. Later, they better optimized the linear part of the method but only for regular patterns [23] because it is based on the frequency domain representations [11]. The methods based on transforms generally implicitly take CFAs as being regular and uniform, and the methods with the discrete Fourier transform are generally applied to regular, uniform and periodic CFAs, since the discrete Fourier transform generally does not map irregular, nonuniform or aperiodic signals into impulses at some frequency points, which makes very difficult to represent and analyze CFAs in the frequency domain, not so easy as for the periodic CFAs as shown in [11].…”

mentioning

confidence: 99%

Universal Demosaicking of Color Filter Arrays

Zhang

Wang

et al. 2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

A large number of color filter arrays (CFAs), periodic or aperiodic, have been proposed. To reconstruct images from all different CFAs and compare their imaging quality, a universal demosaicking method is needed. This paper proposes a new universal demosaicking method based on inter-pixel chrominance capture and optimal demosaicking transformation. It skips the commonly used step to estimate the luminance component at each pixel, and thus, avoids the associated estimation error. Instead, we directly use the acquired CFA color intensity at each pixel as an input component. Two independent chrominance components are estimated at each pixel based on the inter-pixel chrominance in the window, which is captured with the difference of CFA color values between the pixel of interest and its neighbors. Two mechanisms are employed for the accurate estimation: distance-related and edge-sensing weighting to reflect the confidence levels of the inter-pixel chrominance components, and pseudoinverse-based estimation from the components in a window. Then from the acquired CFA color component and two estimated chrominance components, the three primary colors are reconstructed by a linear color transform, which is optimized for the least transform error. Our experiments show that the proposed method is much better than other published universal demosaicking methods.

show abstract

mentioning

confidence: 99%

Universal Demosaicking of Color Filter Arrays

Zhang

Wang

et al. 2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Algorithms in the first category attempt to derive mathematical models by incorporating handcrafted model information based on the properties of RGBW CFAs. For example, the linear relationship between W and RGB pixels [8], [11], frequency structures of CFAs [26], image colorization [13], sparse representation [14], and lowdimensional manifolds [15] have been employed. However, these algorithms require substantial computational resources to solve optimization problems, making them unsuitable to deploy on resource-constrained mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Learned Kernel-Based Interpolation for Efficient RGBW Remosaicing

Vien,

Lee

2023

IEEE Access

View full text Add to dashboard Cite

RGBW remosaicing is an interpolation technique that converts RGBW images captured using RGBW color filtering arrays into Bayer images. Although recent learning-based approaches using convolutional neural networks have shown substantial performance improvements, most algorithms require high computational and memory complexities, which limit their practical applicability. In this work, we propose an efficient and effective RGBW remosaicing algorithm based on learned kernel-based interpolation. First, the proposed algorithm extracts deep feature maps from input RGBW images. Then, we develop a learned kernel-based interpolation module composed of local and non-local interpolation blocks that generates two intermediate Bayer images. Specifically, the local interpolation block learns local filters to recover a Bayer image, whereas the non-local interpolation block recovers a Bayer image by estimating the non-local filters of dynamic shapes. Finally, a reconstructed Bayer image is obtained by combining the complementary information from the intermediate Bayer images using a spatially weighted fusion block. Experimental results demonstrate that the proposed algorithm achieves comparable or even better performance than stateof-the-art algorithms while providing the lowest computational and memory complexities.INDEX TERMS RGBW color filter array (CFA), Bayer CFA, remosaicing, learned kernel-based interpolation.

show abstract

“…Depending on the filter used, a different number of algorithm iterations are done in order to achieve that smoothness. In [16] Singh, M. and Singh, T. (2012) suggest a universal algorithm designed for regular color filters. Just like Condat's algorithm it works in the frequency domain and uses a luminance and two chromatic components.…”

Section: Existing Universal Demosaicing Algorithms -A Breif Descriptionmentioning

confidence: 99%

UDP – a New Universal Demosaicing Pipeline Algorithm for RGB Color Filter Arrays

Zapryanov¹

2014

Information Technologies and Control

View full text Add to dashboard Cite

Linear universal demosaicking of regular pattern color filter arrays

Cited by 6 publications

References 9 publications

Universal Demosaicking of Color Filter Arrays

Universal Demosaicking of Color Filter Arrays

Learned Kernel-Based Interpolation for Efficient RGBW Remosaicing

UDP – a New Universal Demosaicing Pipeline Algorithm for RGB Color Filter Arrays

Contact Info

Product

Resources

About