Embedded Processing for Extended Depth of Field Imaging Systems: From Infinite Impulse Response Wiener Filter to Learned Deconvolution.

Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a neural network. This can be used either to see sharp objects from a greater distance or to reduce manufacturing costs due to tolerance regarding the sensor position. Here, we study the case of an embedded processing with only one convolution with a finite kernel size.

Learning local depth regression from defocus blur by soft-assignment encoding.

We present a novel, to the best of our knowledge, patch-based approach for depth regression from defocus blur. Most state-of-the-art methods for depth from defocus (DFD) use a patch classification approach among a set of potential defocus blurs related to a depth, which induces errors due to the continuous variation of the depth. Here, we propose to adapt a simple classification model using a soft-assignment encoding of the true depth into a membership probability vector during training and a regression scale to predict intermediate depth values.

Learning scene and blur model for active chromatic depth from defocus.

In this paper, we propose what we believe is a new monocular depth estimation algorithm based on local estimation of defocus blur, an approach referred to as depth from defocus (DFD). Using a limited set of calibration images, we directly learn image covariance, which encodes both scene and blur (i.e.

Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments.

In the context of underwater robotics, the visual degradation induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, many underwater localization methods are based on expensive navigation sensors associated with acoustic positioning. On the other hand, pure visual localization methods have shown great potential in underwater localization but the challenging conditions, such as the presence of turbidity and dynamism, remain complex to tackle.

Theoretical performance model for single image depth from defocus.

In this paper we present a performance model for depth estimation using single image depth from defocus (SIDFD). Our model is based on an original expression of the Cramér-Rao bound (CRB) in this context. We show that this model is consistent with the expected behavior of SIDFD.