用暗通道辅助单图去模糊测深度，提升场景结构还原效果。
Dark Channel-Assisted Depth-from-Defocus from a Single Image
- 利用暗通道与模糊程度的关联建模深度线索
- 在真实数据上实现可量化的单图深度估计
- 适合做单目深度感知的视觉系统开发者参考
本文通过引入暗通道作为互补先验，从单张模糊图像中估计场景深度，利用其对局部统计和场景结构的捕捉能力。传统深度估计方法需多张不同光圈下的图像，而单图深度估计因欠约束问题研究较少。本方法基于局部模糊度与对比度变化的关系，构建深度线索以改善场景结构重建。整个流程采用端到端对抗学习训练。在真实数据上的实验表明，将暗通道先验融入单图去模糊深度估计，能有效获得有意义的深度结果，验证了该方法的有效性。
原文摘要 · Abstract (English)
We estimate scene depth from a single defocus-blurred image using the dark channel as a complementary cue, leveraging its ability to capture local statistics and scene structure. Traditional depth-from-defocus (DFD) methods use multiple images with varying apertures or focus. Single-image DFD is underexplored due to its inherent challenges. Few attempts have focused on depth-from-defocus (DFD) from a single defocused image because the problem is underconstrained. Our method uses the relationship between local defocus blur and contrast variations as depth cues to improve scene structure estimation. The pipeline is trained end-to-end with adversarial learning. Experiments on real data demonstrate that incorporating the dark channel prior into single-image DFD provides meaningful depth estimation, validating our approach.
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