Fragile Watermarking for Color Image Recovery Based on Color Filter Array Interpolation

2010 ◽  
pp. 537-543 ◽  
Author(s):  
Zhenxing Qian ◽  
Guorui Feng ◽  
Yanli Ren
Keyword(s):  
Color Image ◽  
Fragile Watermarking ◽  
Image Recovery ◽  
Color Filter ◽  
Color Filter Array ◽  
Filter Array

scholarly journals Data-Driven Convolutional Model for Digital Color Image Demosaicing

2021 ◽  
Vol 11 (21) ◽  
pp. 9975
Author(s):  
Francesco de Gioia ◽  
Luca Fanucci
Keyword(s):  
Color Image ◽  
Random Noise ◽  
Spectral Characteristics ◽  
Model Performance ◽  
Data Driven ◽  
Quantization Noise ◽  
Color Filter ◽  
Parallel Composition ◽  
Color Filter Array ◽  
Filter Array

Modern digital cameras use specific arrangement of Color Filter Array to sample light wavelength corresponding to visible colors. The most common Color Filter Array is the Bayer filter that samples only one color per pixel. To recover the full resolution image, an interpolation algorithm can be used. This process is called demosaicing and it is one of the first processing stages of a digital imaging pipeline. We introduce a novel data-driven model for demosaicing that takes into account the different requirements for reconstruction of the image Luma and Chrominance channels. The final model is a parallel composition of two reconstruction networks with individual architecture and trained with distinct loss functions. In order to solve the overfitting problem, we prepared a dataset that contains groups of patches that share common chromatic and spectral characteristics. We reported the reconstruction error on noise-free images and measured the effect of random noise and quantization noise in the demosaicing reconstruction. To test our model performance, we implemented the network on NVIDIA Jetson Nano, obtaining an end-to-end running time of less than one second for a full frame 12 MPixel image.

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.

scholarly journals Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2970 ◽  
Author(s):  
Yunjin Park ◽  
Sukho Lee ◽  
Byeongseon Jeong ◽  
Jungho Yoon
Keyword(s):  
Color Image ◽  
Image Sensor ◽  
Training Data ◽  
Color Filter ◽  
Color Filter Array ◽  
Image Prior ◽  
Filter Array ◽  
Monochrome Image ◽  
Deep Image ◽  
Different Levels

A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method.

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
Sign in / Sign up

Export Citation Format

Share Document