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.

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 An Enhanced Lossless Color Filter Array Image Compression Based On Predictive Adaptive Arithmetic Coding

2015 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
L.M. Varalakshmi ◽  
R. Sowmiya
Keyword(s):  
Arithmetic Coding ◽  
Color Filter ◽  
Digital Cameras ◽  
Single Image ◽  
Color Information ◽  
Color Filter Array ◽  
Prediction Scheme ◽  
Filter Array ◽  
Adaptive Arithmetic Coding ◽  
Hierarchical Scheme

Most consumer digital cameras use a single image light sensor which provides color information using color filter array(CFA).This  provided a mosaic images, in which each pixel position contains only one color component in case of Bayer CFA Pattern. This paper produced a CFA hierarchical prediction scheme based on context adaptive coding. In CFA hierarchical scheme, the green pixels were subdivided into two sets .  was encoded by a gray scale conventional method and  was  predicted based on . The red pixels were predicted using both the sets of green pixels and blue pixels were predicted using red and green. The predictors were designed based on direction of the edges in the neighborhood. Using the prediction information, the magnitude of prediction error was also determined and context adaptive arithmetic coding was applied to reduce bits. The simulated results on CFA images showed that the proposed method gives less bits per pixel than the recently developed CFA compression algorithms.

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.

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.

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.

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.

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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