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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2691–2700 of 3874 papers

TitleStatusHype
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
Efficient Light Field Reconstruction via Spatio-Angular Dense NetworkCode0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
Del-Net: A Single-Stage Network for Mobile Camera ISP—0
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
Improving Multi-View Stereo via Super-Resolution—0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
Show:102550
← PrevPage 270 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified