Papers Stereo Image Super-Resolution
“Stereo Image Super-Resolution” 태그가 달린 논문 25편 · 필터 해제
Multi-scale interaction network for stereo image super-resolution
Stereo image super-resolution aims to generate high-resolution images by leveraging complementary information from binocular systems. Although previous studies have achieved impressive results, the potential of intra-vie…
Stereo Image Super-ResolutionStereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representation
Stereo image super-resolution (SSR) aims to enhance high-resolution details by leveraging information from stereo image pairs. However, existing stereo super-resolution (SSR) upsampling methods (e.g., pixel shuffle) ofte…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionLearning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution
Stereo image super-resolution (stereoSR) strives to improve the quality of super-resolution by leveraging the auxiliary information provided by another perspective. Most approaches concentrate on refining module design, …
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionNTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results
This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-resolution stereo image pair to a high-r…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionvalidDIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution
We introduce DiffSteISR, a pioneering framework for reconstructing real-world stereo images. DiffSteISR utilizes the powerful prior knowledge embedded in pre-trained text-to-image model to efficiently recover the lost te…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionTAGASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolution
Despite advances in the paradigm of pre-training then fine-tuning in low-level vision tasks, significant challenges persist particularly regarding the increased size of pre-trained models such as memory usage and trainin…
Image Super-Resolutionparameter-efficient fine-tuningStereo Image Super-ResolutionSuper-ResolutionLearning Accurate and Enriched Features for Stereo Image Super-Resolution
Stereo image super-resolution (stereoSR) aims to enhance the quality of super-resolution results by incorporating complementary information from an alternative view. Although current methods have shown significant advanc…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionNAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-Resolution
Stereo image super-resolution (SR) refers to the reconstruction of a high-resolution (HR) image from a pair of low-resolution (LR) images as typically captured by a dual-camera device. To enhance the quality of SR images…
Edge DetectionImage Super-ResolutionSSIMStereo Image Super-Resolution+1Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution
Stereo image super-resolution utilizes the cross-view complementary information brought by the disparity effect of left and right perspective images to reconstruct higher-quality images. Cascading feature extraction modu…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionToward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information
Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems. Although existing methods for single-image super-resolution can be applied to improve stereo i…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionHybrid Transformer and CNN Attention Network for Stereo Image Super-resolution
Multi-stage strategies are frequently employed in image restoration tasks. While transformer-based methods have exhibited high efficiency in single-image super-resolution tasks, they have not yet shown significant advant…
Data AugmentationImage EnhancementImage RestorationImage Super-Resolution+2SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge
Stereo Image Super-Resolution (stereoSR) has attracted significant attention in recent years due to the extensive deployment of dual cameras in mobile phones, autonomous vehicles and robots. In this work, we propose a ne…
Autonomous VehiclesImage RestorationImage Super-ResolutionStereo Image Super-Resolution+1Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
This work aims to improve the applicability of diffusion models in realistic image restoration. Specifically, we enhance the diffusion model in several aspects such as network architecture, noise level, denoising steps, …
Bokeh Effect RenderingDenoisingImage DehazingImage Restoration+5Cross-View Hierarchy Network for Stereo Image Super-Resolution
Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views. To attain superior performance, many methods have prioritized designin…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionPFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution
Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous methods have achieved promising results, …
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionSwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods. However, advances like SwinIR adopts the window-based…
Data AugmentationImage ReconstructionImage RestorationImage Super-Resolution+2Perception-Oriented Stereo Image Super-Resolution
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on improving quantitative evaluation metrics …
Disparity EstimationImage Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionNTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. This challenge has 1…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionNAFSSR: Stereo Image Super-Resolution Using NAFNet
Stereo image super-resolution aims at enhancing the quality of super-resolution results by utilizing the complementary information provided by binocular systems. To obtain reasonable performance, most methods focus on fi…
Image RestorationImage Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionFeedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Under stereo settings, the problem of image super-resolution (SR) and disparity estimation are interrelated that the result of each problem could help to solve the other. The effective exploitation of correspondence betw…
Disparity EstimationImage ReconstructionImage Super-ResolutionStereo Image Super-Resolution+1