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

Kwan, Chiman; Chou, Bryan

doi:10.3390/jimaging5080068

Cited by 15 publications

(23 citation statements)

References 31 publications

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“…In [47], a new pansharpening approach was proposed to demosaic RGBW patterns. In [48], a further improved version by combining deep learning and pansharpening was proposed. In [49], a comparative study of the performance of CFA 1.0 and CFA 2.0 for low lighting images was carried out.…”

Section: Image Demosaicingmentioning

confidence: 99%

Introductory Chapter: Recent Advances in Image Restoration

Kwan¹

2020

Recent Advances in Image Restoration With Applications to Real World Problems

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Section: Image Demosaicingmentioning

confidence: 99%

Introductory Chapter: Recent Advances in Image Restoration

Kwan¹

2020

Recent Advances in Image Restoration With Applications to Real World Problems

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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%

“…A pansharpening approach to demosaicing CFA 2.0 images [16].Recently, we further improved the pansharpening approach by integrating it with deep learning. This approach was summarized in a recent paper [52]. The key idea is to apply deep learning to improve two steps.…”

mentioning

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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“…For instance, the standard approach for CFA 2.0 is still applicable to CFA 3.0. The pan-sharpening based algorithms for CFA 2.0 [ 12 ] and deep learning based algorithms for CFA 2.0 [ 14 ] are also applicable to CFA 3.0. We will describe those details in Section 2 .…”

Section: Introductionmentioning

confidence: 99%

Demosaicing of CFA 3.0 with Applications to Low Lighting Images

Kwan

Larkin

Ayhan

2020

Sensors

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Low lighting images usually contain Poisson noise, which is pixel amplitude-dependent. More panchromatic or white pixels in a color filter array (CFA) are believed to help the demosaicing performance in dark environments. In this paper, we first introduce a CFA pattern known as CFA 3.0 that has 75% white pixels, 12.5% green pixels, and 6.25% of red and blue pixels. We then present algorithms to demosaic this CFA, and demonstrate its performance for normal and low lighting images. In addition, a comparative study was performed to evaluate the demosaicing performance of three CFAs, namely the Bayer pattern (CFA 1.0), the Kodak CFA 2.0, and the proposed CFA 3.0. Using a clean Kodak dataset with 12 images, we emulated low lighting conditions by introducing Poisson noise into the clean images. In our experiments, normal and low lighting images were used. For the low lighting conditions, images with signal-to-noise (SNR) of 10 dBs and 20 dBs were studied. We observed that the demosaicing performance in low lighting conditions was improved when there are more white pixels. Moreover, denoising can further enhance the demosaicing performance for all CFAs. The most important finding is that CFA 3.0 performs better than CFA 1.0, but is slightly inferior to CFA 2.0, in low lighting images.

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

Cited by 15 publications

References 31 publications

Introductory Chapter: Recent Advances in Image Restoration

Introductory Chapter: Recent Advances in Image Restoration

Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

Demosaicing of CFA 3.0 with Applications to Low Lighting Images

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