End-to-End Learning for Image Burst Deblurring
This addresses image quality improvement for burst photography applications, but appears incremental as it combines existing techniques.
The authors tackled multi-frame blind deconvolution for image burst deblurring by proposing a hybrid neural network architecture that combines explicit filter prediction and Fourier coefficient averaging, achieving competitive performance in both quality and runtime.
We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. Our system is trained end-to-end using standard backpropagation on a set of artificially generated training examples, enabling competitive performance in multi-frame blind deconvolution, both with respect to quality and runtime.