EBDNet: Integrating Optical Flow With Kernel Prediction for Burst Denoising

Burst denoising aims to generate a clean image based on a sequence of noisy frames of the same scene captured in quick succession. However, relative motions inevitably happen between frames due to the movements of scenes or cameras, which would lead to blur and ghosting in the generated images. To address this issue, in this paper we propose a novel Efficient Burst Denoising Network (EBDNet) by integrating optical flow estimation with kernel prediction network in an end-to-end scenario. First, a lightweight Denoising Optical Flow Estimation (DOFE) module is presented for both burst feature and image alignment, which encourages to reduce the noise effect when making optical flow estimation. Building upon the aligned burst features and frames, a new fast Fourier convolution-enhanced kernel prediction module is introduced to merge the complementary information. It employs an encoder-decoder architecture with a well-designed feature enrichment block, which exploits the multi-level information from the encoder to boost the decoder features from both spatial and frequency domain views. Extensive experiments demonstrate that the proposed network achieves the best performance compared with state-of-the-art methods while maintaining reasonably low computing complexity.