Reversing Demosaicking and Compression in Color Filter Array Image Processing: Performance Analysis and Modeling

2006 ◽  
Vol 15 (11) ◽  
pp. 3261-3278 ◽  
Author(s):  
N.-X. Lian ◽  
L. Chang ◽  
V. Zagorodnov ◽  
Y.-P. Tan
Keyword(s):  
Image Processing ◽  
Performance Analysis ◽  
Color Filter ◽  
Color Filter Array ◽  
Filter Array

Performance Analysis on Yamanaka Patterned Color Filter Array

2013 ◽  
Vol 717 ◽  
pp. 497-500
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Performance Analysis ◽  
Digital Camera ◽  
Color Filter ◽  
Color Information ◽  
Color Filter Array ◽  
Imaging Device ◽  
Full Resolution ◽  
Filter Array ◽  
Cmos Sensor ◽  
Simulation Results

Generally, a digital camera employs a single CCD or CMOS sensor. In a color imaging device, the color information is usually obtained in sub-sampled patterns of red, green and blue pixels. Thus, full-resolution color is afterward created from this sub-sampled CFA image. This process is normally called as demosaicking. In this paper, we analyze performance of Yamanaka patterned CFA in terms of CPSNR and S-CIELAB. We show the simulation results on test images.

Toward blind joint demosaicing and denoising of raw color filter array data

2021 ◽  
Author(s):  
Fangfang Wu ◽  
Tao Huang ◽  
Weisheng Dong ◽  
Guangming Shi ◽  
Zhonglong Zheng ◽  
...  
Keyword(s):  
Color Filter ◽  
Color Filter Array ◽  
Array Data ◽  
Filter Array

scholarly journals Adaptive Wiener filter super-resolution of color filter array images

2013 ◽  
Vol 21 (16) ◽  
pp. 18820 ◽  
Author(s):  
Barry K. Karch ◽  
Russell C. Hardie
Keyword(s):  
Wiener Filter ◽  
Super Resolution ◽  
Color Filter ◽  
Color Filter Array ◽  
Filter Array

Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

2018 ◽  
Vol 10 (4) ◽  
pp. 140-155 ◽  
Author(s):  
Lu Liu ◽  
Yao Zhao ◽  
Rongrong Ni ◽  
Qi Tian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Color Filter ◽  
Interpolation Algorithm ◽  
Post Processing ◽  
Classification Result ◽  
Filter Array ◽  
Cfa Interpolation ◽  
Learned Features

This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately localize the tampered regions, which combines color filter array (CFA) features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.

Sign in / Sign up

Export Citation Format

Share Document