We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the stateof-the-art across a wide range of noise levels on both real and synthetic data.
Recently, Mildenhall et al. REF proposed a convolutional neural network architecture conceptually similar to non-local means to predict spatially varying kernels that can both align and denoise frames.
Burst Denoising with Kernel Prediction Networks
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science