Burst Denoising via Temporally Shifted Wavelet Transforms
Mobile photography has made great strides in recent years. However, low light imaging still remains a challenge. Long exposures can improve signal-to-noise ratio (SNR) but undesirable motion blur can occur when capturing dynamic scenes. As a result, imaging pipelines often rely on computational photography to improve SNR by fusing multiple short exposures. Recent deep neural network-based methods have been shown to generate visually pleasing results by fusing these exposures in a sophisticated manner, but often at a higher computational cost.
We propose an end-to-end trainable burst denoising pipeline which jointly captures high-resolution and high-frequency deep features derived from wavelet transforms. In our model, precious local details are preserved in high-frequency sub-band features to enhance the final perceptual quality, while the low-frequency sub-band features carry structural information for faithful reconstruction and final objective quality. The model is designed to accommodate variable-length burst captures via temporal feature shifting while incurring only marginal computational overhead. Lastly, we train our model with a realistic noise model for the generalization to real environments. Using these techniques, our method attains state-of-the-art performance on perceptual quality, while being an order of magnitude faster."