Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images. Recent networks mainly resort to image decomposition techniques with complex multi-branch architectures. However, the fixed decomposition techniques would largely restricts their power on versatile images. To exploit the potential power of decomposition mechanism, in this paper, we generalize it from the image domain to the broader feature domain. To this end, we propose a lightweight Feature Decomposition Aggregation Network (FDAN). In particular, we design a Feature Decomposition Block (FDB) to achieve learnable separation of detail and base feature maps, and develop a Hierarchical Feature Decomposition Group by cascading FDBs for powerful multi-level feature decomposition. Moreover, to better evaluate the comparison methods, we collect a large-scale dataset for joint SR-ITM, i.e., SRITM-4K, which provides versatile scenarios for robust model training and evaluation. Experimental results on two benchmark datasets demonstrate that our FDAN is efficient and outperforms state-of-the-art methods on joint SR-ITM. The code of our FDAN and the SRITM-4K dataset are available at https://github.com/CS-GangXu/FDAN.

翻译：联合超分辨率与逆色调映射（Joint SR-ITM）旨在提升低分辨率、标准动态范围图像的分辨率和动态范围。现有网络主要依赖图像分解技术，并采用复杂的多分支架构。然而，固定的分解技术会严重限制其对多样化图像的处理能力。为充分发挥分解机制的潜力，本文将其从图像域推广至更广泛的特征域。为此，我们提出一种轻量级特征分解聚合网络（FDAN）。具体而言，我们设计了特征分解模块（FDB）以实现细节特征图与基础特征图的可学习分离，并通过级联FDB构建层次化特征分解组，从而实现强大的多层级特征分解。此外，为更好地评估对比方法，我们收集了一个用于联合SR-ITM的大规模数据集——SRITM-4K，该数据集包含多样化场景，支持鲁棒的模型训练与评估。在两个基准数据集上的实验结果表明，我们的FDAN在联合SR-ITM任务上高效且优于现有最先进方法。我们的FDAN代码及SRITM-4K数据集已开源至https://github.com/CS-GangXu/FDAN。