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The RealDefocus Benchmark for Defocus Deblurring

2026-07-23 09:09:58
Tim Seizinger, Zhuyun Zhou, Radu Timofte
arXiv_CV
       
Abstract

Single-Image Defocus Deblurring (SIDD) aims to recover an all-in-focus image from a single defocused observation, but rigorous and reproducible evaluation remains challenging due to the scarcity of realistic, high-resolution datasets with well-aligned defocused/sharp pairs and standardized protocols. We build on RealDefocus, a benchmark derived from the real-world RealBokeh dataset originally proposed for Bokeh Rendering. RealDefocus provides paired defocused inputs and sharp ground truth images, predefined training/validation/test splits, and a unified evaluation framework for comparing image restoration and neural rendering approaches. We further outline a benchmarking protocol with cross-dataset validation to assess reconstruction quality and generalization. The project page is publicly available at: this http URL.

Abstract (translated)

URL

https://arxiv.org/abs/2607.21078

PDF

https://arxiv.org/pdf/2607.21078.pdf

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