scholarly journals Comparison of Denoising Algorithms for Demosacing Low Lighting Images Using CFA 2.0

2020 ◽  
Vol 11 (5) ◽  
pp. 37-60
Author(s):  
Chiman Kwan ◽  
Jude Larkin
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Poisson Noise ◽  
Color Filter ◽  
Digital Cameras ◽  
Critical Step ◽  
Lighting Conditions ◽  
Bayer Pattern ◽  
Filter Array ◽  
The Impact

In modern digital cameras, the Bayer color filter array (CFA) has been widely used. It is also widely known as CFA 1.0. However, Bayer pattern is inferior to the red-green-blue-white (RGBW) pattern, which is also known as CFA 2.0, in low lighting conditions in which Poisson noise is present. It is well known that demosaicing algorithms cannot effectively deal with Poisson noise and additional denoising is needed in order to improve the image quality. In this paper, we propose to evaluate various conventional and deep learning based denoising algorithms for CFA 2.0 in low lighting conditions. We will also investigate the impact of the location of denoising, which refers to whether the denoising is done before or after a critical step of demosaicing. Extensive experiments show that some denoising algorithms can indeed improve the image quality in low lighting conditions. We also noticed that the location of denoising plays an important role in the overall demosaicing performance.

scholarly journals Demosaicing of CFA 3.0 with Applications to Low Lighting Images

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3423 ◽  
Author(s):  
Chiman Kwan ◽  
Jude Larkin ◽  
Bulent Ayhan
Keyword(s):  
Comparative Study ◽  
Poisson Noise ◽  
Color Filter ◽  
Signal To Noise ◽  
Color Filter Array ◽  
Lighting Conditions ◽  
Bayer Pattern ◽  
Filter Array ◽  
Better Than

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.

scholarly journals Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1444 ◽  
Author(s):  
Chiman Kwan ◽  
Jude Larkin
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Color Filter ◽  
Signal To Noise ◽  
Lighting Conditions ◽  
Bayer Pattern ◽  
Filter Array ◽  
Better Than

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.

scholarly journals Further Improvements of CFA 3.0 by Combining Inpainting and Pansharpening Techniques

2020 ◽  
Vol 11 (6) ◽  
pp. 1-19
Author(s):  
Chiman Kwan ◽  
Jude Larkin
Keyword(s):  
Deep Learning ◽  
Color Filter ◽  
Digital Cameras ◽  
Color Filter Array ◽  
Filter Array

Color Filter Array (CFA) has been widely used in digital cameras. There are many variants of CFAs in the literature. Recently, a new CFA known as CFA 3.0 was proposed by us and has been shown to yield reasonable performance as compared to some standard ones. In this paper, we investigate the use of inpainting algorithms to further improve the demosaicing performance of CFA 3.0. Six conventional and deep learning based inpainting algorithms were compared. Extensive experiments demonstrated that one algorithm improved over other approaches.

scholarly journals An Improved Demosaicking Algorithm Based On Region-Dividing

2020 ◽  
Vol 4 (5) ◽  
Author(s):  
Zheyuan Chen
Keyword(s):  
Image Quality ◽  
Experimental Results ◽  
Color Filter ◽  
Digital Cameras ◽  
Color Information ◽  
Color Filter Array ◽  
Filter Array ◽  
Color Channels

The Bayer Color Filter Array (CFA) is commonly used in such industries as digital cameras. However, due to the arrangement of color channels in the Bayer CFA, it becomes a problem to estimate the missed color information in each pixel. The algorithms that deal with this problem are named "demosaicking algorithms". There are many demosaicking algorithms, which show different efficiency and image quality for different images. This paper proposes an algorithm that combines two existing algorithms to reach better image qualities and acceptable computing complexities. The experimental results indicate effectiveness in terms of the balance between complexity and quality.

scholarly journals Reversible color transform for Bayer color filter array images

2013 ◽  
Vol 2 ◽  
Author(s):  
Suvit Poomrittigul ◽  
Masanori Ogawa ◽  
Masahiro Iwahashi ◽  
Hitoshi Kiya
Keyword(s):  
Color Images ◽  
Color Filter ◽  
Statistical Relation ◽  
Color Filter Array ◽  
Computational Load ◽  
Bayer Pattern ◽  
Filter Array ◽  
Color Transform ◽  
Karhunen Loeve Transform ◽  
Color Components

In this paper, we propose a reversible color transform (RCT) for color images acquired through a Bayer pattern color filter array. One existing RCT with fixed coefficients is simple to implement. However, it is not adaptive to each of input images. Another existing RCT based on eigenvector of covariance matrix of color components, which is equivalent to Karhunen–Loève transform (KLT), is adaptive. However, it requires heavy computational load. We remove a redundant part of this existing method, utilizing fixed statistical relation between two green components at different locations. Comparing to the KLT-based existing RCT, it was observed that the proposed RCT keeps adaptability and has better coding performance, even though its computational load is reduced.

scholarly journals Comparison of Deep Learning and Conventional Demosaicing Algorithms for Mastcam Images

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 308 ◽  
Author(s):  
Chiman Kwan ◽  
Bryan Chou ◽  
James Bell III
Keyword(s):  
Deep Learning ◽  
Image Quality Assessment ◽  
Imaging System ◽  
Subjective Evaluation ◽  
Ground Truth ◽  
Assessment Model ◽  
Blue Color ◽  
Digital Cameras ◽  
Bayer Pattern ◽  
The Right

Bayer pattern filters have been used in many commercial digital cameras. In National Aeronautics and Space Administration’s (NASA) mast camera (Mastcam) imaging system, onboard the Mars Science Laboratory (MSL) rover Curiosity, a Bayer pattern filter is being used to capture the RGB (red, green, and blue) color of scenes on Mars. The Mastcam has two cameras: left and right. The right camera has three times better resolution than that of the left. It is well known that demosaicing introduces color and zipper artifacts. Here, we present a comparative study of demosaicing results using conventional and deep learning algorithms. Sixteen left and 15 right Mastcam images were used in our experiments. Due to a lack of ground truth images for Mastcam data from Mars, we compared the various algorithms using a blind image quality assessment model. It was observed that no one algorithm can work the best for all images. In particular, a deep learning-based algorithm worked the best for the right Mastcam images and a conventional algorithm achieved the best results for the left Mastcam images. Moreover, subjective evaluation of five demosaiced Mastcam images was also used to compare the various algorithms.

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