Depth Estimation from a Single Image Based on Cauchy Distribution Model

• 作者: Ying Ming
• 来源期刊: 电脑和通信(英文)
• 学科: 数学
• 关键词: Depth Estimation Depth From Defocus Defocus Blur Gaussian Gradient Cauchy Distribution Point Spread Function (PSF)
• 年，卷（期）: 2021,（3）
• 页码范围: 133-142
• 页数: 10页
• 分类号: O17

摘要

Most approaches to estimate a scene’s 3D depth from a single image often model the point spread function (PSF) as a 2D Gaussian function. However, those method<span>s</span><span> are suffered from some noises, and difficult to get a high quality of depth recovery. We presented a simple yet effective approach to estimate exactly the amount of spatially varying defocus blur at edges, based on </span><span>a</span><span> Cauchy distribution model for the PSF. The raw image was re-blurred twice using two known Cauchy distribution kernels, and the defocus blur amount at edges could be derived from the gradient ratio between the two re-blurred images. By propagating the blur amount at edge locations to the entire image using the matting interpolation, a full depth map was then recovered. Experimental results on several real images demonstrated both feasibility and effectiveness of our method, being a non-Gaussian model for DSF, in providing a better estimation of the defocus map from a single un-calibrated defocused image. These results also showed that our method </span><span>was</span><span> robust to image noises, inaccurate edge location and interferences of neighboring edges. It could generate more accurate scene depth maps than the most of existing methods using a Gaussian based DSF model.</span>