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
this https URL

利用双镜头进行超分辨率图像重建，首先通过全局和局部变形将参考图像与低分辨率图像对齐，然后利用无核匹配策略完成图像配准，解决了分辨率差异和保持一致性的问题，并通过构建数据集验证了方法的有效性。

KeDuSR：通过无核匹配实现真实世界双镜头超分辨率

KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free Matching

Even though the collaboration between traditional and neuromorphic event
cameras brings prosperity to frame-event based vision applications, the
performance is still confined by the resolution gap crossing two modalities in
both spatial and temporal domains. This paper is devoted to bridging the gap by
increasing the temporal resolution for images, i.e., motion deblurring, and the
spatial resolution for events, i.e., event super-resolving, respectively. To
this end, we introduce CrossZoom, a novel unified neural Network (CZ-Net) to
jointly recover sharp latent sequences within the exposure period of a blurry
input and the corresponding High-Resolution (HR) events. Specifically, we
present a multi-scale blur-event fusion architecture that leverages the
scale-variant properties and effectively fuses cross-modality information to
achieve cross-enhancement. Attention-based adaptive enhancement and
cross-interaction prediction modules are devised to alleviate the distortions
inherent in Low-Resolution (LR) events and enhance the final results through
the prior blur-event complementary information. Furthermore, we propose a new
dataset containing HR sharp-blurry images and the corresponding HR-LR event
streams to facilitate future research. Extensive qualitative and quantitative
experiments on synthetic and real-world datasets demonstrate the effectiveness
and robustness of the proposed method. Codes and datasets are released at
this https URL

本文致力于通过提高图像的时间分辨率和事件的空间分辨率来弥合传统相机和神经形态相机之间的分辨率差距，并介绍了一种新型的神经网络（CZ-Net）用于同时恢复模糊输入在曝光期内的尖锐隐藏序列和相应的高分辨率事件。

交叉聚焦：同时进行运动去模糊和事件超分辨率

CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving

Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising
paradigm to enhance a low-resolution (LR) input image by introducing an
additional high-resolution (HR) reference image. Existing Ref-SR methods mostly
rely on implicit correspondence matching to borrow HR textures from reference
images to compensate for the information loss in input images. However,
performing local transfer is difficult because of two gaps between input and
reference images: the transformation gap (e.g. scale and rotation) and the
resolution gap (e.g. HR and LR). To tackle these challenges, we propose
C2-Matching in this work, which produces explicit robust matching crossing
transformation and resolution. 1) For the transformation gap, we propose a
contrastive correspondence network, which learns transformation-robust
correspondences using augmented views of the input image. 2) For the resolution
gap, we adopt a teacher-student correlation distillation, which distills
knowledge from the easier HR-HR matching to guide the more ambiguous LR-HR
matching. 3) Finally, we design a dynamic aggregation module to address the
potential misalignment issue. In addition, to faithfully evaluate the
performance of Ref-SR under a realistic setting, we contribute the
Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario.
Extensive experiments demonstrate that our proposed C2-Matching significantly
outperforms state of the arts by over 1dB on the standard CUFED5 benchmark.
Notably, it also shows great generalizability on WR-SR dataset as well as
robustness across large scale and rotation transformations.

本文提出了一种名为 C2-Matching 的方法，用于通过引入高分辨率参考图像增强低分辨率输入图像。该方法解决了输入和参考图像之间的尺度、旋转和分辨率差异等挑战，并在基准测试 CUFED5 数据集上比现有的 Ref-SR 方法表现出更高的性能，同时还表现出在大尺度和旋转变换方面的稳健性。同时本文贡献了一个名为 WR-SR 的数据集，模拟实际使用场景。