The RealDefocus Benchmark for Defocus Deblurring

• URL: http://arxiv.org/abs/2607.21078v1
• Date: Thu, 23 Jul 2026 09:09:58 GMT
• Title: The RealDefocus Benchmark for Defocus Deblurring
• Abstract summary: Single-Image Defocus Deblurring (SIDD) aims to recover an all-in-focus image from a single defocused observation.<n>We build on RealDefocus, a benchmark derived from the real-world RealBokeh dataset.
• Score: 49.159098203027504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: www.github.com/TimSeizinger/RealDefocus-Benchmark.

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