Data-Driven Convolutional Model for Digital Color Image Demosaicing
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...
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MDPI AG
2021
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oai:doaj.org-article:cb29d4bd318b4e9195214d6ce096b0a42021-11-11T15:03:30ZData-Driven Convolutional Model for Digital Color Image Demosaicing10.3390/app112199752076-3417https://doaj.org/article/cb29d4bd318b4e9195214d6ce096b0a42021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9975https://doaj.org/toc/2076-3417Modern 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.Francesco de GioiaLuca FanucciMDPI AGarticledemosaicingbayer filtercolor filter arrayconvolutional neural networkimage processingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9975, p 9975 (2021) |
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demosaicing bayer filter color filter array convolutional neural network image processing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
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demosaicing bayer filter color filter array convolutional neural network image processing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Francesco de Gioia Luca Fanucci Data-Driven Convolutional Model for Digital Color Image Demosaicing |
description |
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. |
format |
article |
author |
Francesco de Gioia Luca Fanucci |
author_facet |
Francesco de Gioia Luca Fanucci |
author_sort |
Francesco de Gioia |
title |
Data-Driven Convolutional Model for Digital Color Image Demosaicing |
title_short |
Data-Driven Convolutional Model for Digital Color Image Demosaicing |
title_full |
Data-Driven Convolutional Model for Digital Color Image Demosaicing |
title_fullStr |
Data-Driven Convolutional Model for Digital Color Image Demosaicing |
title_full_unstemmed |
Data-Driven Convolutional Model for Digital Color Image Demosaicing |
title_sort |
data-driven convolutional model for digital color image demosaicing |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/cb29d4bd318b4e9195214d6ce096b0a4 |
work_keys_str_mv |
AT francescodegioia datadrivenconvolutionalmodelfordigitalcolorimagedemosaicing AT lucafanucci datadrivenconvolutionalmodelfordigitalcolorimagedemosaicing |
_version_ |
1718437176885116928 |