Blind Image Deblurring Using Multi-Task Convolutional Neural Network and Spatially Variant Recurrent Neural Network
We present a novel method of blind image deblurring, capable of handling spatially variant blur caused by various reasons such as camera shake and object motions. Due to the spatially variant nature of the complex blur, our deblurring method includes both spatially variant and spatially invariant components. Namely, we utilize hybrid neural network consisting of a convolutional neural network and a spatially variant recurrent neural network trained in end-to-end manner. Recurrent neural network is used as a deconvolution operator performed on feature maps extracted from the input image by the convolutional neural network, while per-pixel weights of the recurrent neural network are also produced by the same convolutional neural network, making it solve both tasks at the same time. Such hybrid and multi-tasking design allows us to achieve both low computational complexity and high deblurring quality. Quantitative and qualitative evaluations on the widely used GoPro dataset demonstrate that the proposed method provides a good trade-off in terms of complexity and quality against state-of-the-art algorithms.