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SotA/Computer Vision/Deblurring

Deblurring

37 benchmarks999 papers

Deblurring is a computer vision task that involves removing the blurring artifacts from images or videos to restore the original, sharp content. Blurring can be caused by various factors such as camera shake, fast motion, and out-of-focus objects, and can result in a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene.

<span style="color:grey; opacity: 0.6">( Image credit: Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks )</span>

Benchmarks

Deblurring on GoPro

PSNRSSIM

Deblurring on HIDE (trained on GOPRO)

PSNR (sRGB)SSIM (sRGB)Params (M)

Deblurring on RealBlur-R (trained on GoPro)

SSIM (sRGB)PSNR (sRGB)

Deblurring on RealBlur-J

PSNR (sRGB)SSIM (sRGB)Params(M)

Deblurring on RealBlur-R

PSNR (sRGB)SSIM (sRGB)Params

Deblurring on RealBlur-J (trained on GoPro)

PSNR (sRGB)SSIM (sRGB)

Deblurring on RSBlur

Average PSNRSSIM

Deblurring on MSU BASED

SubjectiveSSIMVMAFLPIPSPSNRERQAv2.0

Deblurring on DVD

PSNRSSIM

Deblurring on Beam-Splitter Deblurring (BSD)

PSNR

Deblurring on BASED

PSNRVMAFERQAv2.0LPIPSSSIM

Deblurring on DVD

PSNR

Deblurring on REDS

Average PSNR

Deblurring on RSBlur (trained on synthetic)

Average PSNR

Deblurring on .

10 Images, 4*4 Stitching, Exact Accuracy

Deblurring on HIDE

PSNR

Deblurring on Second dialogue state tracking challenge

MAE