Deep Convolutional Neural Network for Motion Deblurring
Yu Zhou, Rui Fang, Peng Liu, Kai Liu · Society for Industrial and Applied Mathematics eBooks · 2019
Removing blur caused by camera shake in images has always been a challenging problem in computer vision literature due to its ill-posed nature. So it is very difficult to accurately predict blur kernels. In this paper, we propose deep convolutional neural networks for this debluring task to avoid calculating blur kernels. Compared with others approaches, we used the L2 norm to the loss function. L2 regularization is generally used to optimize the regular term in the objective function, which guarantees the restoration image quality and prevents overfitting caused by too many parameters. The model proposed in this paper realizes image deblurring directly by learning the intrinsic relationship between blurred image and target image.