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.

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 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 Colour Restoration of Image Obtained from CCD Sensor Directly

2014 ◽  
Vol 14 (2) ◽  
pp. 81-91
Author(s):  
Jun Luo ◽  
Ying Chen
Keyword(s):  
Image Data ◽  
Original Data ◽  
Color Filter ◽  
Interpolation Algorithm ◽  
Original Image ◽  
Color Information ◽  
Bilinear Interpolation ◽  
Color Filter Array ◽  
Charge Coupled Device ◽  
Filter Array

Abstract The original image data obtained from Charge-coupled Device (CCD) can be called original data, which is lack of color information. In order to restore the color of original image, firstly, we design a Bayer color filter array, and then we use bilinear interpolation algorithm and smooth hue transition interpolation algorithm to restore the color of original image. However, the hues of adjacent pixels change abruptly by the bilinear interpolation, therefore, we use smooth hue transition interpolation to enhance the edge of original image, and finally we identify the ultimate performance of these interpolation algorithms.

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.

scholarly journals An Effective Bicubic Convolution Interpolation-Based Iterative Luma Optimization for Enhancing Quality in Chroma Subsampling

2021 ◽  
Author(s):  
Kuo-Liang Chung ◽  
Chih-Yuan Huang ◽  
Chen-Wei Kao
Keyword(s):  
Iterative Method ◽  
Color Image ◽  
State Of The Art ◽  
Linear Interpolation ◽  
Distortion Function ◽  
Full Color ◽  
Screen Content ◽  
Modification Method ◽  
Convex Property

<div>Traditionally, prior to compressing an RGB full-color image, for each converted 2x2 CbCr block B<sup>CbCr</sup>, chroma subsampling only downsamples B<sup>CbCr</sup>, but without changing the luma block B<sup>Y</sup> at all. In the current research, a special linear interpolation-based, namely the COPY-based, chroma subsampling-first luma modification (CSFLM) study has attempted to change the luma block for enhancing the quality of the reconstructed RGB full-color image. In this paper, a fast and effective nonlinear interpolation, namely the bicubic convolution interpolation (BCI), based iterative luma modification method for CSFLM is proposed. In our iterative method, a BCI-based distortion function and its convex property proof are first provided. Next, based on the proposed convex distortion function, a pseudoinverse technique is applied to obtain the initial luma modification solution, and then an iterative method is proposed to improve the initial luma modification solution. Based on five testing image datasets, namely the IMAX, Kodak, SCI (screen content images), CI (classical images), and Video datasets, the thorough experimental results have demonstrated that on the newly released Versatile Video Coding (VVC) platform VTM-12.0, our iterative luma modification method achieves substantial quality, execution-time, and quality-bitrate tradeoff improvements when compared with the existing state-of-the-art methods.</div>

scholarly journals An Effective Bicubic Convolution Interpolation-Based Iterative Luma Optimization for Enhancing Quality in Chroma Subsampling

2021 ◽  
Author(s):  
Kuo-Liang Chung ◽  
Chih-Yuan Huang ◽  
Chen-Wei Kao
Keyword(s):  
Iterative Method ◽  
Color Image ◽  
State Of The Art ◽  
Linear Interpolation ◽  
Distortion Function ◽  
Full Color ◽  
Screen Content ◽  
Modification Method ◽  
Convex Property

<div>Traditionally, prior to compressing an RGB full-color image, for each converted 2x2 CbCr block B<sup>CbCr</sup>, chroma subsampling only downsamples B<sup>CbCr</sup>, but without changing the luma block B<sup>Y</sup> at all. In the current research, a special linear interpolation-based, namely the COPY-based, chroma subsampling-first luma modification (CSFLM) study has attempted to change the luma block for enhancing the quality of the reconstructed RGB full-color image. In this paper, a fast and effective nonlinear interpolation, namely the bicubic convolution interpolation (BCI), based iterative luma modification method for CSFLM is proposed. In our iterative method, a BCI-based distortion function and its convex property proof are first provided. Next, based on the proposed convex distortion function, a pseudoinverse technique is applied to obtain the initial luma modification solution, and then an iterative method is proposed to improve the initial luma modification solution. Based on five testing image datasets, namely the IMAX, Kodak, SCI (screen content images), CI (classical images), and Video datasets, the thorough experimental results have demonstrated that on the newly released Versatile Video Coding (VVC) platform VTM-12.0, our iterative luma modification method achieves substantial quality, execution-time, and quality-bitrate tradeoff improvements when compared with the existing state-of-the-art methods.</div>

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