Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

Data-driven depth estimation methods struggle with the generalization outside their training
scenes due to the immense variability of the real-world scenes. This problem can be partially …

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision.
Most approaches recover the depth at each pixel based on the focal setting which exhibits …

Mostly, shape from focus (SFF) methods do not consider any prior to extend the accuracy of
the depth map. Ultimately, even the improved depth map might lack the accurate structure of …

Finding the best focused position is the key to shape from focus (SFF). Focus measure (FM)
operators, sampling step, especially various noises usually cause unstable focused position …

Abstract Depth-from-defocus (DFD), modeling the relationship between depth and defocus
pattern in images, has demonstrated promising performance in depth estimation. Recently …

Depth from defocus is an important mechanism that enables vision systems to perceive
depth. While machine vision has developed several algorithms to estimate depth from the …

Depth estimation is a long-lasting yet important task in computer vision. Most of the previous
works try to estimate depth from input images and assume images are all-in-focus (AiF) …

Most digital cameras use specialized autofocus sensors, such as phase detection, lidar or
ultrasound, to directly measure focus state. However, such sensors increase cost and …

In digital photography, autofocus is a key feature that aids high-quality image capture, and
modern approaches use the phase patterns arising from dual-pixel sensors as important …