Papers Image Super-Resolution
“Image Super-Resolution” 태그가 달린 논문 1,814편 · 필터 해제
Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution
Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct a high-resolution hyperspectral image (HR-HSI) by combining the fine spatial details of a high-resolution multispectral image (HR-MSI) with the rich…
Spectral ReconstructionImage Super-ResolutionSemantic SegmentationUncertainty-Guided Latent Diffusion Models for Faithful Super Resolution
The perception-distortion trade-off poses a fundamental challenge in single-image super-resolution (SR). Although diffusion-based SR methods excel at generating perceptually realistic images, achieving high fidelity rema…
Image Super-ResolutionSFMformer: A Spatial-Frequency Modulation Transformer for Lightweight Image Super-Resolution
Sparse attention mechanisms, which score all token pairs but propagate only the strongest, now underpin the most efficient Transformers for lightweight image super-resolution. This paper observes that sparsification chan…
Image Super-ResolutionSupervising the Path to Fine Scales: GalerkinFlow for Scientific-Field and Image Super-Resolution
Most super-resolution models learn from paired data by supervising only the final high-resolution output. This provides little control over how the prediction should evolve between the downsampled observation and its fin…
Image Super-ResolutionIR275K: A Benchmark for Infrared Multi-Frame Super-Resolution Toward Efficient Remote Sensing
Efficient processing is becoming increasingly important in infrared remote sensing, where satellite constellations produce large volumes of observations under constrained detector resolution, power, and downlink bandwidt…
Multi-Frame Super-ResolutionImage Super-ResolutionRarity-Aware Discrete Diffusion with Spatially Consistent Decoding for Photo-Realistic Image Super-Resolution
Continuous diffusion models have become the dominant paradigm for photo-realistic image Super-Resolution (SR), but they typically formulate reconstruction as continuous signal-level denoising and incorporate semantic pri…
Image Super-ResolutionBeyond Unfolding: 60x Faster One-Stage Unmixing for Closely-Spaced Infrared Small Targets
Due to the optical diffraction limit and long imaging distances, Closely-Spaced Infrared Small Targets (CSIST) typically exhibit energy overlap, manifesting as indistinguishable blobs in infrared images. This ambiguity i…
Image Super-ResolutionEfficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution
Diffusion-based methods have achieved impressive performance in real-world image super-resolution (Real-ISR) by leveraging large pre-trained stable diffusion (SD) models as powerful generative priors. However, these meth…
Image Super-ResolutionRFMSR: Residual Flow Matching for Image Super-Resolution
Image super-resolution (ISR) has witnessed remarkable progress with diffusion models and flow matching. The dominant text-to-image (T2I) based approaches leverage large-scale foundation models as generative priors, achie…
Image Super-ResolutionSimon-SR: Spatially Adaptive Modulation and Visual Prompt Adaptation for Text-Reinforced Super-Resolution
Single Image Super-Resolution (SISR) reconstructs high-quality images from low-resolution inputs. While recent multi-modal methods improve perceptual quality, they remain sensitive to erroneous priors and require expensi…
Image Super-ResolutionPhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution
Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This …
Image Super-ResolutionDICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models
Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and…
Conditional Image GenerationImage Super-ResolutionImage DeblurringStyle TransferLanguage-Assisted Super-Resolution from Real-World Low-Resolution Patches
Single image super-resolution aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs. Training SR models typically requires paired HR-LR data, which is difficult to obtain in reality. As a result…
Image Super-ResolutionD$^{2}$R$^{2}$OSR: Degradation-Disentangled Representation for Real-World Omnidirectional Image Super-Resolution
With the growing demand for immersive visual experiences, high-quality omnidirectional images (ODIs) have become increasingly important. However, limitations in imaging devices and transmission bandwidth often lead to lo…
Computational EfficiencyImage Super-ResolutionLearning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution
In computer graphics, visual content is continuously warped, zoomed and resampled. This occurs when engines upscale frames, users zoom into 3D scenes, or foveated VR applies varying scaling. Handling these transformation…
Image Super-ResolutionFreqOrtho-SR: Frequency-Guided Orthogonal Expert Learning for Real-World Image Super-Resolution
Diffusion prior-based methods have shown impressive results in real-world image super-resolution (ISR), yet two key challenges persist: balancing pixel-level fidelity with semantic quality, and adapting to diverse degrad…
Image Super-ResolutionS1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing
We present S1-Omni-Image, an open-weight unified multimodal model for scientific image understanding, generation, and editing. Unlike general-purpose image generation models, scientific image tasks require not only high-…
Image Super-ResolutionMultimodal ReasoningImage SegmentationImage GenerationInterest Entanglement: The Hidden Barrier to Blind Super-Resolution Optimization
Fidelity and perceptual quality are two inherently competing and conflicting objectives in the image super-resolution (SR) task. Different loss functions focus on these objectives to varying extents. Regression losses en…
Dimensionality ReductionImage Super-ResolutionBlind Super-ResolutionFidelity- and Perception-Aware Local Implicit Attention for Arbitrary-Scale Image Super-Resolution
Arbitrary-scale image super-resolution (ASISR) aims to reconstruct high-resolution images from low-resolution inputs over a continuous range of upscaling factors. While traditional pixel-regression approaches often produ…
Image Super-ResolutionLinear Recurrent Unit with Semantic Modulation for Image Super-Resolution
Linear recurrent unit (LRU), designed with a principled formulation for stable linear recurrence, has demonstrated promising accuracy and robustness on long-range dependency tasks. However, its static parameterization an…
Image Super-Resolution