  Dual-lens super-resolution (SR) is a practical scenario for reference (Ref)
based SR by utilizing the telephoto image (Ref) to assist the super-resolution
of the low-resolution wide-angle image (LR input). Different from general
RefSR, the Ref in dual-lens SR only covers the overlapped field of view (FoV)
area. However, current dual-lens SR methods rarely utilize these specific
characteristics and directly perform dense matching between the LR input and
Ref. Due to the resolution gap between LR and Ref, the matching may miss the
best-matched candidate and destroy the consistent structures in the overlapped
FoV area. Different from them, we propose to first align the Ref with the
center region (namely the overlapped FoV area) of the LR input by combining
global warping and local warping to make the aligned Ref be sharp and
consistent. Then, we formulate the aligned Ref and LR center as value-key
pairs, and the corner region of the LR is formulated as queries. In this way,
we propose a kernel-free matching strategy by matching between the LR-corner
(query) and LR-center (key) regions, and the corresponding aligned Ref (value)
can be warped to the corner region of the target. Our kernel-free matching
strategy avoids the resolution gap between LR and Ref, which makes our network
have better generalization ability. In addition, we construct a DuSR-Real
dataset with (LR, Ref, HR) triples, where the LR and HR are well aligned.
Experiments on three datasets demonstrate that our method outperforms the
second-best method by a large margin. Our code and dataset are available at
https://github.com/ZifanCui/KeDuSR.
