Papers Infrared image super-resolution
“Infrared image super-resolution” 태그가 달린 논문 18편 · 필터 해제
FaithIR: Rethinking Infrared Image Super-Resolution from Perceptual Sharpness to Task Relevant Fidelity
Infrared image super-resolution (IISR) is important for downstream tasks such as object detection and semantic segmentation. Existing IISR methods often produce artificial textures, over-sharpened edges, and spurious hig…
Infrared image super-resolutionSemantic SegmentationObject DetectionDecoupling Cross-Modality Manifold Discrepancy: Leveraging Visible Diffusion Priors for Infrared Super-Resolution
Infrared image super-resolution (IISR) mitigates the limitations imposed by low spatial resolution. Existing methods have recognized that IISR should preserve consistency in global distribution and structural information…
Infrared image super-resolutionThe First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high-resolution (HR) infrared images from lo…
Infrared image super-resolutionDual-Branch Remote Sensing Infrared Image Super-Resolution
Remote sensing infrared image super-resolution aims to recover sharper thermal observations from low-resolution inputs while preserving target contours, scene layout, and radiometric stability. Unlike visible-image super…
Infrared image super-resolutionToward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset
Infrared image super-resolution (IISR) under real-world conditions is a practically significant yet rarely addressed task. Pioneering works are often trained and evaluated on simulated datasets or neglect the intrinsic d…
Infrared image super-resolutionRPT-SR: Regional Prior attention Transformer for infrared image Super-Resolution
General-purpose super-resolution models, particularly Vision Transformers, have achieved remarkable success but exhibit fundamental inefficiencies in common infrared imaging scenarios like surveillance and autonomous dri…
Infrared image super-resolutionAutonomous DrivingGPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution
Infrared Image Super-Resolution (IRSR) is challenged by the low contrast and sparse textures of infrared data, requiring robust long-range modeling to maintain global coherence. While State-Space Models like Mamba offer …
Infrared image super-resolutionLong-range modelingImage RestorationDifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution
Infrared imaging is essential for autonomous driving and robotic operations as a supportive modality due to its reliable performance in challenging environments. Despite its popularity, the limitations of infrared camera…
Autonomous DrivingImage Super-ResolutionInfrared image super-resolutionSuper-ResolutionContourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution
Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image …
Image EnhancementImage Super-ResolutionInfrared image super-resolutionPrompt Learning+1Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution
Thermal imaging, leveraging the infrared spectrum, offers a compelling alternative to visible spectrum (VIS) imagery in challenging environmental conditions like low-light, occlusions, and adverse weather. However, its w…
Image Super-ResolutionInfrared And Visible Image FusionInfrared image super-resolutionSuper-ResolutionInfrared Image Super-Resolution via Lightweight Information Split Network
Single image super-resolution (SR) is an established pixel-level vision task aimed at reconstructing a high-resolution image from its degraded low-resolution counterpart. Despite the notable advancements achieved by leve…
Image ReconstructionImage Super-ResolutionInfrared image super-resolutionSuper-ResolutionIRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model
Infrared image super-resolution demands long-range dependency modeling and multi-scale feature extraction to address challenges such as homogeneous backgrounds, weak edges, and sparse textures. While Mamba-based state-sp…
Image EnhancementImage ReconstructionImage Super-ResolutionInfrared image super-resolution+4LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution
Given the broad application of infrared technology across diverse fields, there is an increasing emphasis on investigating super-resolution techniques for infrared images within the realm of deep learning. Despite the im…
Image Super-ResolutionInfrared image super-resolutionSuper-ResolutionInfrared Image Super-Resolution via GAN
The ability of generative models to accurately fit data distributions has resulted in their widespread adoption and success in fields such as computer vision and natural language processing. In this chapter, we provide a…
Image Super-ResolutionInfrared image super-resolutionSuper-ResolutionTexture and Noise Dual Adaptation for Infrared Image Super-Resolution
Recent efforts have explored leveraging visible light images to enrich texture details in infrared (IR) super-resolution. However, this direct adaptation approach often becomes a double-edged sword, as it improves textur…
Domain AdaptationImage Super-ResolutionInfrared image super-resolutionSuper-ResolutionInfrared Image Super-Resolution: Systematic Review, and Future Trends
Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the developme…
Image Super-ResolutionInfrared image super-resolutionSuper-ResolutionInfrared Image Super-Resolution via Heterogeneous Convolutional WGAN
Image super-resolution is important in many fields, such as surveillance and remote sensing. However, infrared (IR) images normally have low resolution since the optical equipment is relatively expensive. Recently, deep …
Image Super-ResolutionInfrared image super-resolutionSuper-ResolutionInfrared Image Super-Resolution via Transfer Learning and PSRGAN
Recent advances in single image super-resolution (SISR) demonstrate the power of deep learning for achieving better performance. Because it is costly to recollect the training data and retrain the model for infrared (IR)…
Generative Adversarial NetworkImage Super-ResolutionInfrared image super-resolutionSuper-Resolution+1