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 311–320 of 3874 papers

TitleStatusHype
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
Burst Super-Resolution with Diffusion Models for Improving Perceptual QualityCode1
Ship in Sight: Diffusion Models for Ship-Image Super ResolutionCode1
Residual Dense Swin Transformer for Continuous Depth-Independent Ultrasound ImagingCode1
Efficient scene text image super-resolution with semantic guidanceCode1
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Learning Dual-Level Deformable Implicit Representation for Real-World Scale Arbitrary Super-ResolutionCode1
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
Activating Wider Areas in Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified