Blind Deconvolution in Supervised / Unsupervised Learning
Blind deconvolution has been studied in several fields including microscopic image, camera motion deblurring, etc. Previous researches have demonstrated non-blind-deconvolution in supervised learning, but sometimes, convolution kernel is not known. We would like to explore a blind deconvolution problem in both learning schemes: supervised and unsupervised. Especially, for unsupervised scheme, prior knowledges such as positivity on kernels or sparsity on image are considered.
The goal of this project is to learn the deconvolution problems and implement corresponding neural networks.
For the comparison, we will reproduce two baseline methods [1, 2]. Coding language will be python and library will be Tensorflow or Pytorch. This project would require prior experiences about deep learning implementations.
[1] Dmitry Ulyanov et. al., "Deep image prior," CVPR 2018
[2] Li Xu et. al., "Deep convolutional neural network for image deconvolution," NIPS 2014