SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 401–410 of 3874 papers

TitleStatusHype
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM AgentsCode0
LocalSR: Image Super-Resolution in Local Region—0
Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image—0
Deep priors for satellite image restoration with accurate uncertainties—0
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA ApproachCode4
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
TASR: Timestep-Aware Diffusion Model for Image Super-ResolutionCode1
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
RFSR: Improving ISR Diffusion Models via Reward Feedback LearningCode1
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified