DuSRFlow: Dual-Lens Super-Resolution via Parallax-Aware Flow Estimation and Complementary Reference Warping

Fangpu Zhang; Huanjing Yue; Zifan Cui; Shaoxiong Tu; Qirui Yang; Jingyu Yang

cs.CVOct 10, 2026

DuSRFlow: Dual-Lens Super-Resolution via Parallax-Aware Flow Estimation and Complementary Reference Warping

Authors: Fangpu Zhang, Huanjing Yue, Zifan Cui, Shaoxiong Tu, Qirui Yang, Jingyu Yang

Organizations: State Key Laboratory of Smart Power Distribution Equipment and System, Tianjin University and School of Electrical and Information Engineering, Tianjin University · School of Electrical and Information Engineering, Tianjin University, Tianjin, China, 300072 · Huawei Technologies Co., Ltd.

Abstract

Dual-lens super-resolution (DuSR) aims to enhance a wide-angle low-resolution (LR) image by exploiting a telephoto reference (Ref) image. A central challenge in DuSR is reliable reference utilization under large parallax, occlusion, and cross-camera imaging discrepancies. Existing methods often employ off-the-shelf monocular optical flow models to provide geometric priors. However, their assumptions of photometric consistency and smooth motion are violated in dual-lens scenarios, leading to unreliable alignment and degraded reference-based reconstruction. To address these issues, we propose DuSRFlow, a dual-lens super-resolution framework that improves reference utilization from both correspondence learning and reference aggregation. First, we introduce DuFlowNet, a dedicated dual-lens flow estimator for robust cross-view alignment. To train DuFlowNet without ground-truth flow annotations, we develop a real--synthetic data fusion strategy that composites synthetic foreground objects onto real dual-lens image pairs, generating controllable large-parallax samples while preserving realistic cross-camera imaging characteristics. Second, we observe that reference reliability is highly spatially uneven in DuSR, where unreliable local reference responses may suppress the utilization of reliable cues. We therefore propose a Complementary Reference Warping strategy that coordinates patch-based warping and kernel-free-guided deformable warping according to local reference reliability, producing a more reliable reference representation before LR--Ref fusion. In addition, we construct DuSR-RealV2, a challenging real-world benchmark with complex depth variations, large parallax, and occlusion. Extensive experiments on multiple DuSR benchmarks demonstrate that DuSRFlow achieves state-of-the-art performance in reconstruction quality, perceptual quality, and cross-dataset generalization.

Figures & tables

Fig. 1: (a) LR–Ref patches suffer from cross-camera discrepancies and parallax, challenging generic optical flow estimation. (b) Benefiting from real–synthetic data fusion, our dual-lens flow achieves cleaner alignment than SPyNet with fewer warping artifacts. (c) More accurate alignment improves reference utilization, leading to more faithful structures and fewer reference-imprinted artifacts than KeDuSR.

Fig. 2: Although the central reference regions have the same quality in both cases, the final SR results differ because unreliable corner references reduce the overall reliability of the fused reference representation.

Fig. 3: Distributions of Occlusion Rate (OR) and Average Flow Magnitude (AFM) across different DuSR datasets. DuSR-RealV2-Paired exhibits the lowest and most compact OR distribution, indicating highly reliable paired alignment. In contrast, DuSR-RealV2-Unpaired shows a broader distribution shifted toward larger AFM values, demonstrating stronger LR–Ref parallax and higher evaluation difficulty.

Fig. 4: The framework of our DuSRFlow. The proposed dual-lens flow based alignment module first aligns the reference image $\mathbf{I}^{\mathrm{Ref}}$ to the LR center region, yielding the aligned reference feature $\hat{\mathbf{F}}^{\mathrm{Ref}}$ . In parallel, kernel-free (KF) matching between $\mathbf{I}^{\mathrm{LR}}$ and $\mathbf{I}^{\mathrm{LRC}}$ produces the KF index and confidence map, while the SISR encoder extracts the LR feature $\mathbf{F}^{\mathrm{LR}}$ . These features are then processed by the proposed complementary reference warping to generate the aggregated reference feature $\tilde{\mathbf{F}}^{\mathrm{Ref}}$ , which is fused with the LR feature through LR–Ref fusion to produce the final SR result $\mathbf{I}^{\mathrm{SR}}$ .

Fig. 5: (a) Illustration of dual-lens parallax and view-dependent occlusion. A foreground object can exhibit different projections and occlude different background regions across views. (b) The proposed real–synthetic data fusion strategy. A synthetic foreground is composited onto real dual-lens image pairs, where different homography transformations are applied to the reference branch and the LR/HR-center branches to generate controllable parallax while preserving realistic dual-lens imaging characteristics.

Fig. 6: A toy experiment to evaluate DuFlowNet. (a) HR image reconstruction with pixel-domain patch matching using different matching methods. (b) Patch similarity and hit rate curves with different matching methods.

Fig. 7: Validation of KF confidence as a proxy for cross-camera reference-transfer reliability. All valid patches are divided into 20 equal-count bins according to KF confidence

C

, and each point reports the mean

C

and mean ideal reference confidence

C^{\mathrm{ideal}}

within each bin. The Spearman correlation over all patches is

0.637

Method	Full-Image	Center-Image	Corner-Image
Method	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$	PSNR/SSIM	PSNR/SSIM
RCAN- $\ell$	27.16 / 0.8256 / 0.266	27.77 / 0.8298	27.02 / 0.8242
RCAN	26.25 / 0.7866 / 0.145	26.85 / 0.7860	26.12 / 0.7868
SwinIR- $\ell$	26.76 / 0.8156 / 0.277	27.39 / 0.8199	26.62 / 0.8142
SwinIR	26.50 / 0.7875 / 0.151	27.04 / 0.7867	26.38 / 0.7878
ESRGAN	26.39 / 0.7861 / 0.145	26.92 / 0.7867	26.27 / 0.7858

TABLE I: Quantitative comparisons on DuSR-RealV2-Paired. Bold and underlined indicate best and second-best, respectively. -

\ell

denotes training with only reconstruction loss.

Method	Full-Image	Center-Image	Corner-Image
Method	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$	PSNR/SSIM	PSNR/SSIM
RCAN- $\ell$	26.44 / 0.8676 / 0.147	26.91 / 0.8704	26.33 / 0.8667
SwinIR- $\ell$	26.14 / 0.8601 / 0.157	26.35 / 0.8612	26.11 / 0.8597
ESRGAN	25.78 / 0.8622 / 0.152	25.91 / 0.8637	25.77 / 0.8617
BSRGAN	24.77 / 0.8227 / 0.202	25.01 / 0.8233	24.71 / 0.8225
TTSR- $\ell$	26.48 / 0.8676 / 0.147	27.69 / 0.8810	26.17 / 0.8631

TABLE II: Quantitative comparisons on DuSR-Real.

Method	NRQM $\uparrow$	NIQE $\downarrow$	PI $\downarrow$	BRISQUE $\downarrow$	MUSIQ $\uparrow$	MANIQA $\uparrow$
RCAN- $\ell$	4.929	5.590	5.391	43.115	53.149	0.3295
SwinIR- $\ell$	4.763	6.066	5.714	42.262	51.087	0.3154
ESRGAN- $\ell$	4.655	6.102	5.775	47.831	52.164	0.3223
BSRGAN- $\ell$	4.763	5.865	5.601	46.540	53.624	0.3379
TTSR- $\ell$	4.655	4.879	4.964	39.636	55.526	0.3507
MASA- $\ell$	4.925	5.202	5.397	44.265	53.118	0.3274

TABLE III: Quantitative comparisons on DuSR-RealV2-Unpaired. Since ground-truth HR images are unavailable, we employ no-reference metrics for objective evaluation.

Method	Adapt.	Paired		Unpaired		Params.	Time
Method	Adapt.	PSNR $\uparrow$	SSIM $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	(M)	(ms)
SPyNet	–	25.51	0.8766	20.60	0.7642	1.4	20
SPyNet	$\checkmark$	33.78 $(+8.3)$	0.9470 $(+.070)$	23.59 $(+3.0)$	0.8075 $(+.043)$	1.4	20
PWC-Net	–	28.60	0.9289	21.46	0.7972	9.4	16
PWC-Net	$\checkmark$	33.72 $(+5.1)$	0.9504 $(+.022)$	23.47 $(+2.0)$	0.8082 $(+.011)$	9.4	16
RAFT	–	27.76	0.8811	20.84	0.7440	5.3	64

TABLE IV: Comparison of optical flow architectures on DuSR-RealV2 using official pretrained weights and the proposed real–synthetic data fusion strategy. Values in parentheses denote changes relative to the corresponding pretrained models. Latency is measured for estimating one

512\times 960

flow field on an NVIDIA RTX 3090 GPU.

Fig. 8: Visual comparisons on the DuSR-RealV2 dataset. The presented results are obtained with only the reconstruction loss. Zoom in for better observation. The Pseudo ground truth of the unpaired subset is provided for reference only.

Variants			Full-Image	Center-Image	Corner-Image
No.	Ref. Warping	Flow Prior	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$	PSNR/SSIM	PSNR/SSIM
#1	Patch Warping	w/o flow	29.07 / 0.8592 / 0.204	35.07 / 0.9330	28.13 / 0.8343
#2	Patch Warping	SPyNet	29.14 / 0.8652 / 0.195	37.22 / 0.9575	28.09 / 0.8341
#3	Patch Warping	DuFlowNet	29.22 / 0.8682 / 0.191	37.64 / 0.9637	28.15 / 0.8359
#4	Patch + DCN	DuFlowNet	29.60 / 0.8677 / 0.194	37.40 / 0.9628	28.55 / 0.8356
#5	Patch + KF-DCN	DuFlowNet	29.76 / 0.8711 / 0.193	38.43 / 0.9683	28.68 / 0.8383

TABLE V: Ablation study on DuFlowNet Prior and KF-DCN

Variants	Full-Image	Center-Image	Corner-Image
Variants	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$	PSNR/SSIM	PSNR/SSIM
#1:Patch warping	29.22 / 0.8682 / 0.191	37.64 / 0.9637	28.15 / 0.8359
#2:KF-DCN warping	29.68 / 0.8686 / 0.197	37.21 / 0.9622	28.65 / 0.8370
#3:Region-concat	29.34 / 0.8673 / 0.195	37.24 / 0.9609	28.30 / 0.8358
#4:Channel-concat	29.59 / 0.8684 / 0.195	38.13 / 0.9663	28.52 / 0.8353
#5:Uniform aggregation	29.64 / 0.8692 / 0.195	37.42 / 0.9625	28.61 / 0.8376

TABLE VI: Ablation study on the Complementary Reference Warping

Fig. 9: Visual comparison of optical flow based alignment results on the DuSR-RealV2-Unpaired subset. The artifacts in the figure indicate the displacement differences between the aligned results and the LR images.

Fig. 10: Visualization of reference feature aggregation results. Green and red boxes indicate center and corner regions, respectively.

Fig. 11: Failure case under extremely large parallax. The white dashed box indicates the center region, and the yellow box marks the enlarged area. The color artifacts in the Ref image visualize the large LR–Ref displacement. Under such extreme parallax, DuSRFlow still exhibits residual fusion artifacts, although fewer than KeDuSR.

Method	Full-Image	Center-Image	Corner-Image
Method	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$	PSNR/SSIM	PSNR/SSIM
RCAN- $\ell$	25.96 / 0.8033 / 0.234	25.69 / 0.7937	26.12 / 0.8065
SwinIR- $\ell$	25.78 / 0.7982 / 0.246	25.50 / 0.7885	25.94 / 0.8015
TTSR- $\ell$	25.92 / 0.8017 / 0.235	25.94 / 0.7962	25.98 / 0.8036
MASA- $\ell$	25.95 / 0.7989 / 0.239	25.81 / 0.7899	26.07 / 0.8020
DATSR- $\ell$	25.81 / 0.7975 / 0.242	25.58 / 0.7882	25.95 / 0.8007

TABLE S1: Quantitative comparisons on RealMCVSR-Real.

	Full-Image	Center-Image	Corner-Image
Method	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$	PSNR/SSIM	PSNR/SSIM
RCAN- $\ell$	25.67 / 0.8049 / 0.308	26.65 / 0.8158	25.45 / 0.8012
SwinIR- $\ell$	25.32 / 0.8007 / 0.315	25.81 / 0.8073	25.22 / 0.7985
TTSR- $\ell$	25.83 / 0.8044 / 0.311	26.75 / 0.8188	25.62 / 0.7996
MASA- $\ell$	25.78 / 0.8030 / 0.303	26.70 / 0.8155	25.58 / 0.7988
DCSR- $\ell$	26.02 / 0.8123 / 0.293	28.37 / 0.8440	25.51 / 0.8016

TABLE S2: Quantitative comparisons on CameraFusion-Real.

Method	DuSR-Real	RealMCVSR-Real	CameraFusion-Real
Method	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$	PSNR $\uparrow$ /SSIM $\uparrow$ /LPIPS $\downarrow$
TTSR- $\ell$	24.59 / 0.8374 / 0.175	23.15 / 0.7513 / 0.273	25.58 / 0.8018 / 0.279
MASA- $\ell$	24.84 / 0.8270 / 0.198	24.40 / 0.7575 / 0.285	25.43 / 0.7947 / 0.289
DATSR- $\ell$	24.58 / 0.8278 / 0.196	24.01 / 0.7610 / 0.271	25.17 / 0.7917 / 0.288
DCSR- $\ell$	25.30 / 0.8497 / 0.169	24.69 / 0.7807 / 0.251	25.56 / 0.8054 / 0.276
SelfDZSR- $\ell$	25.79 / 0.8402 / 0.188	24.82 / 0.7703 / 0.274	26.35 / 0.8057 / 0.290

TABLE S3: Generalization evaluation of models trained on DuSR-RealV2.

Dataset	Metrics	w/o parallax sim.	Synthetic-only	Ours
Paired	PSNR $\uparrow$	33.72	31.15	33.74
Paired	SSIM $\uparrow$	0.9507	0.9050	0.9510
Unpaired	PSNR $\uparrow$	22.43	22.83	23.29
Unpaired	SSIM $\uparrow$	0.7788	0.7526	0.8164

TABLE S4: Ablation study of our real–synthetic data fusion strategy.

Fig. S1: Visual comparison of alignment results. The red and green boxes highlight large-parallax foreground and background regions, respectively.

Variant	Peak Memory (MiB)
Patch warping only	2371.09
KF-DCN warping only	3502.75
Ours	3600.75

TABLE S5: Inference-time peak GPU memory of different warping configurations.

Method	SISR				RefSR
Method	RCAN	SwinIR	ESRGAN	BSRGAN	TTSR	MASA	DATSR	DCSR	SelfDZSR	ZeDuSR	KeDuSR	Ours
Latency (s)	0.69	2.85	0.08	0.45	7.51	1.52	9.35	0.84	0.17	180	0.51	0.47
Params (M)	5.63	11.75	16.70	16.61	6.25	4.02	10.51	3.19	0.52	1.49	5.63	5.02
FLOPs (T)	6.15	0.41	1.80	6.77	2.92	2.34	5.81	2.08	1.14	——	2.46	2.76

TABLE S6: FLOPs and latency are measured for generating an HR output of size

1792\times 896

on a single NVIDIA RTX 3090 GPU.

Fig. S2: Visual comparisons on RealMCVSR-Real, DuSR-Real, and CameraFusion-Real datasets. Zoom in for better observation.

Fig. S3: Visual comparisons of SR results on the DuSR-RealV2 dataset. The upper part shows results on the paired subset, and the lower part shows results on the unpaired subset. The presented results are obtained with only the reconstruction loss. For the unpaired subset, the pseudo ground truth is provided for reference only. Zoom in for better observation.

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