Demosaising using a Convolutional Neural Network approach
(2017) In Master's Theses in Mathematical Sciences FMA820 20171Mathematics (Faculty of Engineering)
- Abstract
- This thesis is about investigating the feasibility to use convolutional neural networks as a demosaicing approach. Three loss methods and different layer structures have been evaluated as well as changing different parameters and layers in the convolutional neural network to find which changes are beneficial to make a neural network perform demosaicing well.
The convolutional neural network has been compared to a fully convolutional neural network, a multilayer perceptron and the Hamilton Adams demosaicing algorithm. The prospect of demosaicing raw image sensor data and images with noise was also investigated.
The conclusion is that a convolutional neural network can indeed perform demosaicing with good results, even when using a... (More) - This thesis is about investigating the feasibility to use convolutional neural networks as a demosaicing approach. Three loss methods and different layer structures have been evaluated as well as changing different parameters and layers in the convolutional neural network to find which changes are beneficial to make a neural network perform demosaicing well.
The convolutional neural network has been compared to a fully convolutional neural network, a multilayer perceptron and the Hamilton Adams demosaicing algorithm. The prospect of demosaicing raw image sensor data and images with noise was also investigated.
The conclusion is that a convolutional neural network can indeed perform demosaicing with good results, even when using a small and less complex network. The convolutional neural network was also able to demosaic raw images as well as remove noise from images, although with not as good result as when demosaicing artificial data. The convolutional neural network on average performed demosaicing with a peak signal to noise ratio of 34 dB. This compares to the Hamilton Adams method that has a peak signal to noise ratio of 37 dB, although when measured with the structural similarity our method performs better than the Hamilton Adams method. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8916452
- author
- Dammer, Karin LU and Grosz, Ronja
- supervisor
- organization
- course
- FMA820 20171
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Convolutional neural networks, fully convolutional neural network, demosaicing, image sensor data, noise reduction
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3321-2017
- ISSN
- 1404-6342
- other publication id
- 2017:E30
- language
- English
- id
- 8916452
- date added to LUP
- 2017-06-22 16:17:25
- date last changed
- 2017-06-22 16:17:25
@misc{8916452,
abstract = {{This thesis is about investigating the feasibility to use convolutional neural networks as a demosaicing approach. Three loss methods and different layer structures have been evaluated as well as changing different parameters and layers in the convolutional neural network to find which changes are beneficial to make a neural network perform demosaicing well.
The convolutional neural network has been compared to a fully convolutional neural network, a multilayer perceptron and the Hamilton Adams demosaicing algorithm. The prospect of demosaicing raw image sensor data and images with noise was also investigated.
The conclusion is that a convolutional neural network can indeed perform demosaicing with good results, even when using a small and less complex network. The convolutional neural network was also able to demosaic raw images as well as remove noise from images, although with not as good result as when demosaicing artificial data. The convolutional neural network on average performed demosaicing with a peak signal to noise ratio of 34 dB. This compares to the Hamilton Adams method that has a peak signal to noise ratio of 37 dB, although when measured with the structural similarity our method performs better than the Hamilton Adams method.}},
author = {{Dammer, Karin and Grosz, Ronja}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Theses in Mathematical Sciences}},
title = {{Demosaising using a Convolutional Neural Network approach}},
year = {{2017}},
}