Improving Bracket Image Restoration and Enhancement with Flow-guided
  Alignment and Enhanced Feature Aggregation
Wenjie Lin Zhen Liu Chengzhi Jiang Mingyan Han Ting Jiang Shuaicheng Liu
In this paper, we address the Bracket Image Restoration and Enhancement
(BracketIRE) task using a novel framework, which requires restoring a
high-quality high dynamic range (HDR) image from a sequence of noisy, blurred,
and low dynamic range (LDR) multi-exposure RAW inputs. To overcome this
challenge, we present the IREANet, which improves the multiple exposure
alignment and aggregation with a Flow-guide Feature Alignment Module (FFAM) and
an Enhanced Feature Aggregation Module (EFAM). Specifically, the proposed FFAM
incorporates the inter-frame optical flow as guidance to facilitate the
deformable alignment and spatial attention modules for better feature
alignment. The EFAM further employs the proposed Enhanced Residual Block (ERB)
as a foundational component, wherein a unidirectional recurrent network
aggregates the aligned temporal features to better reconstruct the results. To
improve model generalization and performance, we additionally employ the Bayer
preserving augmentation (BayerAug) strategy to augment the multi-exposure RAW
inputs. Our experimental evaluations demonstrate that the proposed IREANet
shows state-of-the-art performance compared with previous methods.