Debayering RGBW color filter arrays: A pansharpening approach

Kwan, Chiman; Chou, Bryan; Kwan, Li-Yun Martin; Budavari, Bence

doi:10.1109/uemcon.2017.8248995

Cited by 17 publications

(51 citation statements)

References 24 publications

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“…We also have two fusion based algorithms known as F3 and ATMF, which were used in our earlier studies [16,30,52]. F3 fuses the three best performing algorithms and ATMF fuses seven high-performing algorithms.…”

Section: Pansharpeningmentioning

confidence: 99%

“…In the past two decades, there are numerous other CFA patterns mentioned in [9][10][11][12][13][14][15]. For images collected in normal illumination conditions, our earlier papers [16] concluded that CFA 1.0 is better than CFA 2.0. In the CFA research community, one common belief is that CFA 2.0 has better performance for images taken in low lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

“…We have seen good performance improvement.Figure 4. A combination of deep learning and pansharpening approach to demosaicing CFA 2.0.In addition to the above mentioned algorithms for CFA 2.0, we also applied least-squares lumachroma demultiplexing (LSLCD) over [53] in our experiments.We also have two fusion based algorithms known as F3 and ATMF, which were used in our earlier studies [16,30,52]. F3 fuses the three best performing algorithms and ATMF fuses seven highperforming algorithms.It should be noted that algorithms for CFA 2.0 are much fewer than those of CFA 1.0.…”

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confidence: 99%

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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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Section: Pansharpeningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

Kwan

Larkin

2019

Electronics

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“…A variant of the Bayer pattern, known as CFA2.0 ( Figure 6), was introduced in [59]. Recently, a new approach [60] to debayering was introduced based on pansharpening ideas. A thorough comparative study was performed using benchmark datasets.…”

Section: Application Of Pansharpening Algorithms To Debayeringmentioning

confidence: 99%

A New Pansharpening Approach for Hyperspectral Images

Kwan¹,

Zhou²,

Budavari³

2018

Colorimetry and Image Processing

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“…Due to the fact that half of the pixels in the RGBW pattern are white, we think that it is appropriate to apply pansharpening techniques to perform the demosaicing. Although RGBW has some robustness against noise and low light conditions, it is not popular and does not have good performance [4] as compared to the standard Bayer pattern. Nevertheless, we would like to argue that the debayering of RGBW is a good research problem for academia even in the case where mosaiced images are clean and noise-free.…”

Section: Introductionmentioning

confidence: 99%

Further Improvement of Debayering Performance of RGBW Color Filter Arrays Using Deep Learning and Pansharpening Techniques

Kwan

Chou

2019

J. Imaging

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The RGBW color filter arrays (CFA), also known as CFA2.0, contains R, G, B, and white (W) pixels. It is a 4 × 4 pattern that has 8 white pixels, 4 green pixels, 2 red pixels, and 2 blue pixels. The pattern repeats itself over the whole image. In an earlier conference paper, we cast the demosaicing process for CFA2.0 as a pansharpening problem. That formulation is modular and allows us to insert different pansharpening algorithms for demosaicing. New algorithms in interpolation and demosaicing can also be used. In this paper, we propose a new enhancement of our earlier approach by integrating a deep learning-based algorithm into the framework. Extensive experiments using IMAX and Kodak images clearly demonstrated that the new approach improved the demosaicing performance even further.

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Debayering RGBW color filter arrays: A pansharpening approach

Cited by 17 publications

References 24 publications

Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

A New Pansharpening Approach for Hyperspectral Images

Further Improvement of Debayering Performance of RGBW Color Filter Arrays Using Deep Learning and Pansharpening Techniques

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