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 1701–1725 of 3874 papers

TitleStatusHype
Blind inverse problems with isolated spikes—0
Detecting disruption of HER2 membrane protein organization in cell membranes with nanoscale precision—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
Analysis and Interpretation of Deep CNN Representations as Perceptual Quality Features—0
Detail-Enhancing Framework for Reference-Based Image Super-Resolution—0
Detailed 3D Human Body Reconstruction from Multi-view Images Combining Voxel Super-Resolution and Learned Implicit Representation—0
Blind Image Super-Resolution via Contrastive Representation Learning—0
Blind Image Super-Resolution: A Survey and Beyond—0
Designing A Composite Dictionary Adaptively From Joint Examples—0
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis—0
Depth Super-Resolution from Explicit and Implicit High-Frequency Features—0
Analog Neural Computing with Super-resolution Memristor Crossbars—0
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs—0
AccelIR: Task-Aware Image Compression for Accelerating Neural Restoration—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
Depth Super Resolution by Rigid Body Self-Similarity in 3D—0
Depth Separable architecture for Sentinel-5P Super-Resolution—0
Blind Hyperspectral-Multispectral Image Fusion via Graph Laplacian Regularization—0
Depth Anything with Any Prior—0
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches—0
Dense U-net for super-resolution with shuffle pooling layer—0
Densely Connected High Order Residual Network for Single Frame Image Super Resolution—0
Blaze3DM: Marry Triplane Representation with Diffusion for 3D Medical Inverse Problem Solving—0
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified