  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.
