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 1001–1010 of 3874 papers

TitleStatusHype
Towards Progressive Multi-Frequency Representation for Image WarpingCode0
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolutionCode0
Diffusion Models, Image Super-Resolution And Everything: A Survey—0
Exposure Bracketing Is All You Need For A High-Quality ImageCode2
Compressing Deep Image Super-resolution Models—0
UGPNet: Universal Generative Prior for Image Restoration—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-ResolutionCode2
Noise-free Optimization in Early Training Steps for Image Super-ResolutionCode1
KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free MatchingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified