scholarly journals CVNet: confidence voting convolutional neural network for camera spectral sensitivity estimation

2021 ◽  
Author(s):  
Junfei Shen ◽  
Tianyue He ◽  
Mingwei Zhou ◽  
Qican Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spectral Sensitivity ◽  
Sensitivity Estimation ◽  
Camera Spectral Sensitivity

scholarly journals 3D color charts for camera spectral sensitivity estimation

2017 ◽  
Author(s):  
Rada Deeb ◽  
Damien Muselet ◽  
Mathieu Hebert ◽  
Alain Tremeau ◽  
Joost van de Weijer
Keyword(s):  
Spectral Sensitivity ◽  
Sensitivity Estimation ◽  
Camera Spectral Sensitivity

Classification of hyperspectral images based on a convolutional neural network and spectral sensitivity

2020 ◽  
Vol 21 (3) ◽  
pp. 240-248
Author(s):  
Cheng-ming Ye ◽  
Xin Liu ◽  
Hong Xu ◽  
Shi-cong Ren ◽  
Yao Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spectral Sensitivity ◽  
Hyperspectral Images

scholarly journals Rank-based camera spectral sensitivity estimation

2016 ◽  
Vol 33 (4) ◽  
pp. 589 ◽  
Author(s):  
Graham Finlayson ◽  
Maryam Mohammadzadeh Darrodi ◽  
Michal Mackiewicz
Keyword(s):  
Spectral Sensitivity ◽  
Sensitivity Estimation ◽  
Camera Spectral Sensitivity

scholarly journals Reference data set for camera spectral sensitivity estimation

2015 ◽  
Vol 32 (3) ◽  
pp. 381 ◽  
Author(s):  
Maryam Mohammadzadeh Darrodi ◽  
Graham Finlayson ◽  
Teresa Goodman ◽  
Michal Mackiewicz
Keyword(s):  
Spectral Sensitivity ◽  
Reference Data ◽  
Data Set ◽  
Sensitivity Estimation ◽  
Camera Spectral Sensitivity

A scan-free end-to-end new approach for snapshot camera spectral sensitivity estimation

2021 ◽  
Author(s):  
MingWei Zhou ◽  
Wenjing Chen ◽  
Tianyue He ◽  
Qican Zhang ◽  
Junfei Shen
Keyword(s):  
Spectral Sensitivity ◽  
New Approach ◽  
Sensitivity Estimation ◽  
End To End ◽  
Camera Spectral Sensitivity

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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