Image Quality Evaluation on Modified Bayer CFA

2013 ◽  
Vol 718-720 ◽  
pp. 2050-2054 ◽  
Author(s):  
Gwang Gil Jeon
Keyword(s):  
High Performance ◽  
Quality Evaluation ◽  
Color Image ◽  
Color Filter ◽  
Interpolation Algorithm ◽  
Digital Cameras ◽  
Full Color ◽  
Filter Array ◽  
Color Interpolation ◽  
Almost All

Almost all digital cameras adopt a color filter array to acquire images and requesting a demosaicking process of the sub-sampled color components to have the full color image. Thus, it is necessary to restore the CFA image correctly. Otherwise, perceptible color errors are presented. This paper proposes a color interpolation algorithm based on filter. The CFA we used is modified Bayer CFA. Simulation results show that the proposed method is effective and yield high performance in CPSNR and S-CIELAB.

Optimally Determined Modified Bayer Color Array for Imagery

2013 ◽  
Vol 717 ◽  
pp. 501-505 ◽  
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Digital Camera ◽  
Color Filter ◽  
Color Information ◽  
Full Color ◽  
Bayer Pattern ◽  
Filter Array ◽  
Color Interpolation ◽  
Simulation Results ◽  
Different Color ◽  
Blue Pixel

This paper introduced a problem of the modified Bayer pattern color filter array (CFA). A demosaicking method is also known as color interpolation, which is a digital camera process employed to restore a full-color imagery from an image with missing color information. In general, a CFA pair contains two green pixels and one red and blue pixel (RGGB). However, there exist alternatives such as RRGB or RGBB. In this paper, we study the effect of three different color arrays. Simulation results show that the obtained filters give good performance.

scholarly journals The Effect of the Color Filter Array Layout Choice on State-of-the-Art Demosaicing

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3215 ◽  
Author(s):  
Ana Stojkovic ◽  
Ivana Shopovska ◽  
Hiep Luong ◽  
Jan Aelterman ◽  
Ljubomir Jovanov ◽  
...  
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Image Data ◽  
Linear Interpolation ◽  
Color Filter ◽  
Color Filter Array ◽  
Full Color ◽  
Design Choice ◽  
Filter Array ◽  
The One

Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development in the past 20 years ranges from simple linear interpolation to modern neural-network-based (NN) approaches that encode the prior knowledge of millions of training images to fill in missing data in an inconspicious way. CFA design, on the other hand, is less well studied, although still recognized to strongly impact demosaicing performance. This is because demosaicing algorithms are typically limited to one particular CFA pattern, impeding straightforward CFA comparison. This is starting to change with newer classes of demosaicing that may be considered generic or CFA-agnostic. In this study, by comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing. We test the hypothesis that, with the increasing power of NN-based demosaicing, the influence of optimal CFA design on system performance decreases. This hypothesis is supported with the experimental results. Such a finding would herald the possibility of relaxing CFA requirements, providing more freedom in the CFA design choice and producing high-quality cameras.

Efficient Color Configurations for Sensors

2013 ◽  
Vol 705 ◽  
pp. 319-322
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Performance Comparison ◽  
Color Filter ◽  
Single Pair ◽  
Digital Cameras ◽  
Color Information ◽  
Filter Array ◽  
Single Sensor ◽  
Color Interpolation ◽  
Single Pixel ◽  
Blue Pixel

mageries are acquired by digital cameras using a single sensor covered with a color filter array (CFA). The most generally employed CFA pattern is Bayer CFA. Therefore in the acquired CFA imagery, each pixel includes only one of three colors: they are red, green, and blue. This CFA color interpolation methods reconstruct losing color information of the other two primary colors for every single pixel. In a single pair of Bayer CFA, there are two green pixels and one red pixel and one blue pixel. In this paper, we interchanged green pixel with other colors. The performance comparison is shown in Experimental results section.

scholarly journals Image Demosaicing Based on Generative Adversarial Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Jingrui Luo ◽  
Jie Wang
Keyword(s):  
Color Image ◽  
Color Filter ◽  
Digital Cameras ◽  
Data Preparation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Filter Array ◽  
Discriminant Ability ◽  
Preparation Phase ◽  
Single Sensor

Digital cameras with a single sensor use a color filter array (CFA) that captures only one color component in each pixel. Therefore, noise and artifacts will be generated when reconstructing the color image, which reduces the resolution of the image. In this paper, we proposed an image demosaicing method based on generative adversarial network (GAN) to obtain high-quality color images. The proposed network does not need any initial interpolation process in the data preparation phase, which can greatly reduce the computational complexity. The generator of the GAN is designed using the U-net to directly generate the demosaicing images. The dense residual network is used for the discriminator to improve the discriminant ability of the network. We compared the proposed method with several interpolation-based algorithms and the DnCNN. Results from the comparative experiments proved that the proposed method can more effectively eliminate the image artifacts and can better recover the color image.

Color Interpolation Algorithm for a Periodic White-Dominant RGBW Color Filter Array

2020 ◽  
Vol 2020 (10) ◽  
pp. 134-1-134-5
Author(s):  
Kyeonghoon Jeong ◽  
Jonghyun Kim ◽  
Moon Gi Kang
Keyword(s):  
Interpolation Method ◽  
Sampling Rate ◽  
Color Difference ◽  
Color Filter ◽  
Interpolation Algorithm ◽  
High Sampling Rate ◽  
Filter Array ◽  
Spectrum Band ◽  
Color Interpolation ◽  
Visual Confirmation

Recently, CFA sensors including the W channel have been developed. Since the W channel receives luminance information, the W channel has a wider spectrum band than that of the R, G, and B channels. Therefore, the W channel can get higher SNR than that of the R, G, and B channels. There are many color interpolation methods for the Bayer CFA, but less for the RGBW CFA. In this paper, we propose a new color interpolation method for white-dominant RGBW CFA. The proposed method is edgeadaptive. Its method reconstruct the W channel first which has a high sampling rate. Next, the R, G, and B channels are reconstructed by using the color difference domains with theWchannel. Experimental results show that the PSNR and visual confirmation are higher than the conventional method.

Source Camera Identification Based on In-and-Inter Channel Traces

2013 ◽  
Vol 380-384 ◽  
pp. 3754-3757
Author(s):  
Yong Jie Xie ◽  
Yi Bao ◽  
Sen Feng Tong ◽  
Yu Hao Yang
Keyword(s):  
Model Identification ◽  
Color Filter ◽  
Interpolation Algorithm ◽  
Camera Model ◽  
Accuracy Rate ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Filter Array ◽  
Color Interpolation ◽  
Similarity And Difference

Source camera detecting, which is about establishing whether or not the images of interest are taken by the same camera, is a challenging problem. Most CFA(color filter array) interpolation algorithm characterize the feature of camera which take the image. In this paper, we propose an improved algorithm. Instead of using inter-channel demosaicking/color interpolation traces, we first extract four in-and-inter-channel variance maps, respectively, and then extract the shape, similarity and difference features of maps for camera model identification. The experimental results show that the method had significantly improved the accuracy rate of source digital image identification.

scholarly journals Demosaicing by Differentiable Deep Restoration

2021 ◽  
Vol 11 (4) ◽  
pp. 1649
Author(s):  
Jie Tang ◽  
Jian Li ◽  
Ping Tan
Keyword(s):  
Color Image ◽  
Superior Performance ◽  
Color Filter ◽  
Digital Cameras ◽  
Joint Learning ◽  
Selective Filter ◽  
Full Color ◽  
Spectrally Selective ◽  
Image Patches ◽  
The Difference

A mosaic of color filter arrays (CFAs) is commonly used in digital cameras as a spectrally selective filter to capture color images. The captured raw image is then processed by a demosaicing algorithm to recover the full-color image. In this paper, we formulate demosaicing as a restoration problem and solve it by minimizing the difference between the input raw image and the sampled full-color result. This under-constrained minimization is then solved with a novel convolutional neural network that estimates a linear subspace for the result at local image patches. In this way, the result in an image patch is determined by a few combination coefficients of the subspace bases, which makes the minimization problem tractable. This approach further allows joint learning of the CFA and demosaicing network. We demonstrate the superior performance of the proposed method by comparing it with state-of-the-art methods in both settings of noise-free and noisy data.

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