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 2601–2610 of 3874 papers

TitleStatusHype
Understanding Deformable Alignment in Video Super-Resolution—0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks—0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution—0
Burst Image Super-Resolution with Mamba—0
PatchVSR: Breaking Video Diffusion Resolution Limits with Patch-wise Video Super-Resolution—0
Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning—0
PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution—0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation—0
Burst Image Super-Resolution with Base Frame Selection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified