Burst Denoising via Temporally Shifted Wavelet Transforms

摘要

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.