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.

翻译：本文针对多帧RAW图像恢复与增强（BracketIRE）任务提出了一种新型框架，旨在从一系列含噪声、模糊且低动态范围（LDR）的多曝光RAW输入中恢复高质量的高动态范围（HDR）图像。为攻克这一难题，我们提出了IREANet，通过流引导特征对齐模块（FFAM）和增强特征聚合模块（EFAM）改进了多曝光对齐与聚合过程。具体而言，FFAM将帧间光流作为引导信息，辅助可变形对齐与空间注意力模块以实现更优的特征对齐；EFAM则采用所提出的增强残差块（ERB）作为基础组件，通过单向循环网络聚合对齐后的时序特征以更好地重建结果。为提升模型的泛化能力与性能，我们额外引入了Bayer保持增强策略（BayerAug）对多曝光RAW输入进行数据增强。实验评估表明，与现有方法相比，所提出的IREANet展现出最先进的性能。