Exploitation of inter-color correlation for color image demosaicking

Jaiswal, Sunil Prasad; Au, Oscar C.; Jakhetiya, Vinit; Yuan, Yuan; Yang, Haiyan

doi:10.1109/icip.2014.7025363

Cited by 27 publications

(37 citation statements)

References 7 publications

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“…SSD and FlexIP have similar (slightly superior) cPSNRs to the proposed method, but they are about 69 and 2633 times slower. • The proposed LED has lower cPSNR than RI, ICC, MLRI, CS, DDR, MDWI, ARI and LDINAT, but is about 12,17,27,28,122,280,420 and 4400 times faster than them respectively. Fig.4-Fig.6 show examples that LED works visually favorably to state-of-the-art methods.…”

Section: A Numerical Evaluation On Low Resolution Imagesmentioning

confidence: 88%

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Low Cost Edge Sensing for High Quality Demosaicking

Yan

Ouyang

Zuo

et al. 2019

IEEE Trans. on Image Process.

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Digital cameras that use Color Filter Arrays (CFA) entail a demosaicking procedure to form full RGB images. As today's camera users generally require images to be viewed instantly, demosaicking algorithms for real applications must be fast. Moreover, the associated cost should be lower than the cost saved by using CFA. For this purpose, we revisit the classical Hamilton-Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Based on a close look at HA's strength and weakness, we design a very low cost edge sensing scheme. Briefly, it guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 28 demosaicking algorithms by running their open source codes on benchmark datasets. Compared to methods of similar computational cost, our method achieves substantially higher accuracy; Whereas compared to methods of similar accuracy, our method has significantly lower cost. Moreover, on test images of currently popular resolution, the quality of our algorithm is comparable to top performers, whereas its speed is tens of times faster.

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Section: A Numerical Evaluation On Low Resolution Imagesmentioning

confidence: 88%

“…As this assumption fails at edges, Ref. [12] proposes compensating inter-channel interpolation by intra-channel interpolation if evidence of non-linearity presents; Ref. [21] assumes that the three channels have consistent edge directions; Regularization is investigated to formulate the inter-and intra-channel correlation (e.g., [24] [8]).…”

Section: Introductionmentioning

confidence: 99%

Low Cost Edge Sensing for High Quality Demosaicking

Yan

Ouyang

Zuo

et al. 2019

IEEE Trans. on Image Process.

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“…An additional assumption of the TTP metric is that the target variation is separable, which is a common assumption at least for high spatial frequencies [8][9]:…”

Section: Ttp Metric Color Camera Modelmentioning

confidence: 99%

Modeling demosaicing of color corrected cameras in the NV-IPM

2016

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A critical step in creating an image using a Bayer pattern sampled color camera is demosaicing, the process of combining the individual color channels using a post-processing algorithm to produce the final displayed image. The demosaicing process can introduce degradations which reduce the quality of the final image. These degradations must be accounted for in order to accurately predict the performance of color imaging systems. In this paper, we present analytical derivations of transfer functions to allow description of the effects of demosaicing on the overall system blur and noise. The effects of color balancing and the creation of the luminance channel image are also explored. The methods presented are validated through Monte Carlo simulations, which can also be utilized to determine the transfer functions of non-linear demosaicing methods. Together with this new treatment of demosaicing, the framework behind the color detector component in NV-IPM is discussed.

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“…Details can be found in [32]; • Fusion using three best (F3) [30]: The mean of pixels from demosaiced images of the three best individual methods were used; • Bilinear: Bilinear interpolation is the simplest algorithm that uses the nearest neighbors for interpolation; • Sequential Energy Minimization (SEM) [33]: A deep learning approach based on sequential energy minimization was proposed in [33]. The performance was reasonable, except that the computation takes a long time due to sequential optimization; • Exploitation of Color Correlation (ECC) [34]: The authors of [34] proposed a scheme that exploits the correlation between different color channels much more effectively than some of the existing algorithm; • Minimized-Laplacian Residual Interpolation (MLRI) [35]: This is a residual interpolation (RI)-based algorithm based on a minimized-Laplacian version; • Adaptive Residual Interpolation (ARI) [36]: ARI adaptively combines RI and MLRI at each pixel, and adaptively selects a suitable iteration number for each pixel, instead of using a common iteration number for all of the pixels; • Directional Difference Regression (DDR) [37]: DDR obtains the regression models using directional color differences of the training images. Once models are learned, they will be used for demosaicing.…”

Section: Demosaicing Algorithmsmentioning

confidence: 99%

Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

Kwan

Larkin

2019

Electronics

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It is commonly believed that having more white pixels in a color filter array (CFA) will help the demosaicing performance for images collected in low lighting conditions. However, to the best of our knowledge, a systematic study to demonstrate the above statement does not exist. We present a comparative study to systematically and thoroughly evaluate the performance of demosaicing for low lighting images using two CFAs: the standard Bayer pattern (aka CFA 1.0) and the Kodak CFA 2.0 (RGBW pattern with 50% white pixels). Using the clean Kodak dataset containing 12 images, we first emulated low lighting images by injecting Poisson noise at two signal-to-noise (SNR) levels: 10 dBs and 20 dBs. We then created CFA 1.0 and CFA 2.0 images for the noisy images. After that, we applied more than 15 conventional and deep learning based demosaicing algorithms to demosaic the CFA patterns. Using both objectives with five performance metrics and subjective visualization, we observe that having more white pixels indeed helps the demosaicing performance in low lighting conditions. This thorough comparative study is our first contribution. With denoising, we observed that the demosaicing performance of both CFAs has been improved by several dBs. This can be considered as our second contribution. Moreover, we noticed that denoising before demosaicing is more effective than denoising after demosaicing. Answering the question of where denoising should be applied is our third contribution. We also noticed that denoising plays a slightly more important role in 10 dBs signal-to-noise ratio (SNR) as compared to 20 dBs SNR. Some discussions on the following phenomena are also included: (1) why CFA 2.0 performed better than CFA 1.0; (2) why denoising was more effective before demosaicing than after demosaicing; and (3) why denoising helped more at low SNRs than at high SNRs.The baseline approach refers to a simple upsampling of the reduced resolution color image shown in Figure 2. The standard approach for CFA 2.0 is shown in Figure 2, which illustrates how to combine the interpolated luminance image with the reduced resolution color image to generate a full resolution color image. Electronics 2019, 8, 1444 4 of 56 combine the interpolated luminance image with the reduced resolution color image to generate a full resolution color image.Figure 2. Standard approach to demosaicing CFA 2.0 images. Image from [38].In the paper [16] written by us, we proposed a pansharpening approach to demosaicing CFA 2.0. This approach is illustrated in Figure 3. The missing pixels in the panchromatic band are interpolated. At the same time, the reduced resolution CFA is demosaiced. We then apply pansharpening to generate the full resolution color image. There are many pansharpening algorithms that can be used. Principal Component Analysis (PCA) [39], Smoothing Filter-based Intensity Modulation (SFIM) [40], Modulation Transfer Function Generalized Laplacian Pyramid (GLP) [41], MTF-GLP with High Pass Modulation (HPM) [42], Gram Schmidt (GS) [43], GS Adaptive (G...

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Exploitation of inter-color correlation for color image demosaicking

Cited by 27 publications

References 7 publications

Low Cost Edge Sensing for High Quality Demosaicking

Low Cost Edge Sensing for High Quality Demosaicking

Modeling demosaicing of color corrected cameras in the NV-IPM

Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

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