Papers Blind Super-Resolution
“Blind Super-Resolution” 태그가 달린 논문 72편 · 필터 해제
Interest 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-ResolutionMultispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation
Mapping standing dead trees is crucial for acquiring information on the effects of climate change on forests and forest biodiversity. However, leveraging high-quality aerial imagery for dead tree segmentation poses chall…
Image Super-ResolutionBlind Super-ResolutionDomain AdaptationHistory-Augmented Contrastive Learning With Soft Mixture of Experts for Blind Super-Resolution of Planetary Remote Sensing Images
Blind Super-Resolution (BSR) in planetary remote sensing constitutes a highly ill-posed inverse problem, characterized by unknown degradation patterns and a complete absence of ground-truth supervision. Existing unsuperv…
Blind Super-ResolutionImage ReconstructionContrastive LearningTwo Heads Better than One: Dual Degradation Representation for Blind Super-Resolution
Previous methods have demonstrated remarkable performance in single image super-resolution (SISR) tasks with known and fixed degradation (e.g., bicubic downsampling). However, when the actual degradation deviates from th…
Image Super-ResolutionBlind Super-ResolutionBlind Super Resolution with Reference Images and Implicit Degradation Representation
Previous studies in blind super-resolution (BSR) have primarily concentrated on estimating degradation kernels directly from low-resolution (LR) inputs to enhance super-resolution. However, these degradation kernels, whi…
Blind Super-ResolutionUnsupervised Imaging Inverse Problems with Diffusion Distribution Matching
This work addresses image restoration tasks through the lens of inverse problems using unpaired datasets. In contrast to traditional approaches -- which typically assume full knowledge of the forward model or access to p…
Blind Super-ResolutionDeblurringImage RestorationSuper-ResolutionTaming Stable Diffusion for Computed Tomography Blind Super-Resolution
High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires increased radiation exposure, creating a critical trade-off between image quality and patient safety. While deep learning m…
Blind Super-ResolutionComputed Tomography (CT)Language ModelingLanguage Modelling+2Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations
Prior methodologies have disregarded the diversities among distinct degradation types during image reconstruction, employing a uniform network model to handle multiple deteriorations. Nevertheless, we discover that preva…
Blind Super-ResolutionImage ReconstructionSuper-ResolutionEAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution
Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. While T2I models have traditionally relied on U-Net architectures, recent a…
Blind Super-ResolutionImage RestorationIn-Context LearningSuper-ResolutionKernelFusion: Assumption-Free Blind Super-Resolution via Patch Diffusion
Traditional super-resolution (SR) methods assume an ``ideal'' downscaling SR-kernel (e.g., bicubic downscaling) between the high-resolution (HR) image and the low-resolution (LR) image. Such methods fail once the LR imag…
Blind Super-ResolutionSuper-ResolutionBoosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution
Recently, diffusion-based blind super-resolution (SR) methods have shown great ability to generate high-resolution images with abundant high-frequency detail, but the detail is often achieved at the expense of fidelity. …
Blind Super-ResolutionSuper-ResolutionAdaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution
Blind Super-Resolution(blind SR) aims to enhance the model's generalization ability with unknown degradation, yet it still encounters severe overfitting issues. Some previous methods inspired by dropout, which enhanc…
AttributeBlind Super-ResolutionImage RestorationImage Super-Resolution+1RFSR: Improving ISR Diffusion Models via Reward Feedback Learning
Generative diffusion models (DM) have been extensively utilized in image super-resolution (ISR). Most of the existing methods adopt the denoising loss from DDPMs for model optimization. We posit that introducing reward f…
Blind Super-ResolutionImage Super-ResolutionModel OptimizationSuper-ResolutionBlind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels
In recent years, computational Time-of-Flight (ToF) imaging has emerged as an exciting and a novel imaging modality that offers new and powerful interpretations of natural scenes, with applications extending to 3D, light…
Blind Super-ResolutionSuper-ResolutionContent-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution
Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. Ho…
Blind Super-ResolutionContrastive LearningImage Super-ResolutionSuper-ResolutionImage Super-Resolution with Taylor Expansion Approximation and Large Field Reception
Self-similarity techniques are booming in blind super-resolution (SR) due to accurate estimation of the degradation types involved in low-resolution images. However, high-dimensional matrix multiplication within self-sim…
Blind Super-ResolutionImage Super-ResolutionSuper-ResolutionSuppressing Uncertainties in Degradation Estimation for Blind Super-Resolution
The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model the image degradation process using blur…
Blind Super-ResolutionImage Super-ResolutionSelf-Supervised LearningSuper-ResolutionBlind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
Learning-based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we …
Blind Super-ResolutionImage Super-ResolutionMeta-LearningSuper-ResolutionA Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pre-training on labelled datasets. This paper proposes an unsu…
Blind Super-ResolutionImage RestorationImage Super-ResolutionSuper-ResolutionAddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation
Blind super-resolution methods based on stable diffusion showcase formidable generative capabilities in reconstructing clear high-resolution images with intricate details from low-resolution inputs. However, their practi…
Blind Super-ResolutionSuper-Resolution