scholarly journals Weights-Based Image Demosaicking Using Posteriori Gradients and the Correlation of R–B Channels in High Frequency

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 600
Author(s):  
Meidong Xia ◽  
Chengyou Wang ◽  
Wenhan Ge
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Experimental Results ◽  
Color Filter ◽  
Signal To Noise ◽  
Visual Effect ◽  
B Values ◽  
Bayer Pattern ◽  
Filter Array ◽  
Composite Peak

In this paper, we propose a weights-based image demosaicking algorithm which is based on the Bayer pattern color filter array (CFA). When reconstructing the missing G components, the proposed algorithm uses weights based on posteriori gradients to mitigate color artifacts and distortions. Furthermore, the proposed algorithm makes full use of the correlation of R–B channels in high frequency when interpolating R/B values at B/R positions. Experimental results show that the proposed algorithm is superior to previous similar algorithms in composite peak signal-to-noise ratio (CPSNR) and subjective visual effect. The biggest advantage of the proposed algorithm is the use of posteriori gradients and the correlation of R–B channels in high frequency.

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 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.

A Research on X-Trans Patterned CMOS

2013 ◽  
Vol 705 ◽  
pp. 313-318
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Spatial Resolution ◽  
Digital Camera ◽  
Super Resolution ◽  
Experimental Results ◽  
Color Filter ◽  
Color Filter Array ◽  
Bayer Pattern ◽  
Filter Array

In the last two decades, super-resolution and demosaicking have been researched actively. Generally, digital camera suffers from both color filtering and low spatial resolution. Therefore it is worth studying above issues. In this paper, we design filters to conduct super-resolution for digital camera. Bayer pattern has been widely used in digital camera. In this paper, we conduct experiments on X-Trans color filter array (CFA) pattern. Experimental results confirm that the proposed filters are effective for solving demosaicking issues.

Image Decomposition and Inpainting Based on Besov and Hilbert-Sobolev Space

2014 ◽  
Vol 599-601 ◽  
pp. 1857-1862
Author(s):  
Zhu Qin Liu
Keyword(s):  
Sobolev Space ◽  
Besov Space ◽  
Signal To Noise Ratio ◽  
Image Decomposition ◽  
Experimental Results ◽  
Signal To Noise ◽  
Visual Effect ◽  
Structure Information ◽  
Basic Characteristics ◽  
Application Requirements

An image is decomposed into structure and texture adopt Meyer model , In order to more effectively express the characteristics of the image ,a schem is proposed that structure and texture described use Besov space and Hilbert-Sobolev space respectively,and different inpainting methods is adopt for structure and texture .Experimental results show that the algorithm calculated simple, easy to implement ,Smoothness and structure information, such as the basic characteristics of the image portrayed to meet the application requirements and inpaint results in low signal-to-noise ratio, the visual effect is superior to the similar method.

scholarly journals An Image Denoising Algorithm Based On Curvelet Transform

2016 ◽  
Vol 12 (1) ◽  
pp. 5827-5831
Author(s):  
Yin Kaikai ◽  
Su Bo
Keyword(s):  
Mathematical Method ◽  
Wavelet Transform ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Curvelet Transform ◽  
Experimental Results ◽  
High Peak ◽  
Signal To Noise ◽  
Visual Effect ◽  
Good Image

Aiming at the limitations of the wavelet transform in image denoising, this paper proposes a new image denoising algorithm based on curvelet transform mathematical method. In this paper, the feasibility of this method is proved by the experimental results. The experiment result shows that, using the proposed new algorithm can get high peak signal to noise ratio, visual effect is very good image.

Image Reconstruction on Quincunx Patterned Green Channel

2013 ◽  
Vol 717 ◽  
pp. 493-496
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Image Reconstruction ◽  
Least Squares ◽  
Least Squares Method ◽  
Interpolation Method ◽  
Experimental Results ◽  
Color Filter ◽  
Image Artifacts ◽  
Green Channel ◽  
Filter Array ◽  
Single Sensor

This paper addresses the issue of the quincunx patterned green channel interpolation method that is obtained by single sensor cameras. Our goal is to reconstruct the green channel in Bayer color filter array (CFA) data. We present a new filter-based method for the reduction of image artifacts in green channel. To reconstruct green channel, we trained a filter using least squares method. Experimental results confirm the effectiveness of the proposed method. Compared to other bilinear and bicubic filters, the improvement in quality has been achieved.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

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.

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