depthfromdefocus

Depth from defocus is a computer vision technique used to estimate the depth of a scene from a single or multiple images. The core principle relies on the fact that objects at different distances from a camera will appear with varying degrees of blur. A camera with a fixed aperture and focus will render objects closer to the focal plane sharply, while objects further away will be progressively out of focus.

In a single-image depth from defocus approach, the blur kernel or point spread function, which characterizes the blur, is analyzed. By modeling the relationship between the blur extent and the distance of an object, a depth map can be reconstructed. This often involves estimating the size of the blur circle for different image features.

Multi-image depth from defocus utilizes two or more images taken with different focal settings or aperture sizes. By comparing how the blur changes across these images, more robust and accurate depth estimations can be achieved. For instance, if an object appears sharper in one image and blurrier in another, it suggests a specific depth.

This technique has applications in areas such as 3D reconstruction, augmented reality, and autofocus systems. However, challenges can arise from textureless regions, uniform surfaces, and complex lighting conditions that can affect blur estimation accuracy.