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 1841–1850 of 3874 papers

TitleStatusHype
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
ELSR: Extreme Low-Power Super Resolution Network For Mobile DevicesCode0
Joint Learning Content and Degradation Aware Feature for Blind Super-ResolutionCode1
Laplacian Pyramid-like AutoencoderCode0
3D Super-Resolution Ultrasound with Adaptive Weight-Based Beamforming—0
DSR: Towards Drone Image Super-ResolutionCode1
SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-ResolutionCode1
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-ExemplarsCode1
Sliding Window Recurrent Network for Efficient Video Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified