  Due to the sophisticated imaging process, an identical scene captured by
different cameras could exhibit distinct imaging patterns, introducing distinct
proficiency among the super-resolution (SR) models trained on images from
different devices. In this paper, we investigate a novel and practical task
coded cross-device SR, which strives to adapt a real-world SR model trained on
the paired images captured by one camera to low-resolution (LR) images captured
by arbitrary target devices. The proposed task is highly challenging due to the
absence of paired data from various imaging devices. To address this issue, we
propose an unsupervised domain adaptation mechanism for real-world SR, named
Dual ADversarial Adaptation (DADA), which only requires LR images in the target
domain with available real paired data from a source camera. DADA employs the
Domain-Invariant Attention (DIA) module to establish the basis of target model
training even without HR supervision. Furthermore, the dual framework of DADA
facilitates an Inter-domain Adversarial Adaptation (InterAA) in one branch for
two LR input images from two domains, and an Intra-domain Adversarial
Adaptation (IntraAA) in two branches for an LR input image. InterAA and IntraAA
together improve the model transferability from the source domain to the
target. We empirically conduct experiments under six Real to Real adaptation
settings among three different cameras, and achieve superior performance
compared with existing state-of-the-art approaches. We also evaluate the
proposed DADA to address the adaptation to the video camera, which presents a
promising research topic to promote the wide applications of real-world
super-resolution. Our source code is publicly available at
https://github.com/lonelyhope/DADA.git.
