Abstract
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.
Abstract (translated)
在本文中，我们使用一种新框架来解决Bracket Image Restoration and Enhancement（BracketIRE）任务，该框架需要从噪声、模糊和低动态范围（LDR）的多曝光RAW输入序列中恢复高质量的高动态范围（HDR）图像。为了克服这一挑战，我们提出了IReadNet，它通过引入流量引导特征对齐模块（FFAM）和增强特征聚合模块（EFAM）来改善多曝光对齐和聚合。具体来说，所提出的FFAM利用跨帧光流作为指导，以促进可变形对齐和空间注意模块（更好的特征对齐），而EFAM则进一步采用提出的增强残差块（ERB）作为基本组件，其中单向递归网络聚集对齐的时空特征以更好地重构结果。为了提高模型的泛化能力和性能，我们还使用Bayer preserving augmentation（BayerAug）策略来增强多曝光RAW输入。我们的实验评估结果表明，与以前的方法相比，所提出的IReadNet显示出最先进的性能。
URL
https://arxiv.org/abs/2404.10358
