Deep Depth From Focus

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 maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end learning approach to this problem. One of the main challenges we face is the hunger for data of deep neural networks. In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us to digitally create focal stacks of varying sizes. Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem. We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components. These experiments show that our proposed method `DDFFNet' achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201

Multi-contrast imaging and digital refocusing on a mobile microscope with a domed LED array

We demonstrate the design and application of an add-on device for improving the diagnostic and research capabilities of CellScope--a low-cost, smartphone-based point-of-care microscope. We replace the single LED illumination of the original CellScope with a programmable domed LED array. By leveraging recent advances in computational illumination, this new device enables simultaneous multi-contrast imaging with brightfield, darkfield, and phase imaging modes. Further, we scan through illumination angles to capture lightfield datasets, which can be used to recover 3D intensity and phase images without any hardware changes. This digital refocusing procedure can be used for either 3D imaging or software-only focus correction, reducing the need for precise mechanical focusing during field experiments. All acquisition and processing is performed on the mobile phone and controlled through a smartphone application, making the computational microscope compact and portable. Using multiple samples and different objective magnifications, we demonstrate that the performance of our device is comparable to that of a commercial microscope. This unique device platform extends the field imaging capabilities of CellScope, opening up new clinical and research possibilities

Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs

Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved in separate tracks. In this paper, we present a unified learning-based technique that simultaneously uses both types of cues for depth inference. Specifically, we use a pair of focal stacks as input to emulate human perception. We first construct a comprehensive focal stack training dataset synthesized by depth-guided light field rendering. We then construct three individual networks: a Focus-Net to extract depth from a single focal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image from the focal stack, and a Stereo-Net to conduct stereo matching. We show how to integrate them into a unified BDfF-Net to obtain high-quality depth maps. Comprehensive experiments show that our approach outperforms the state-of-the-art in both accuracy and speed and effectively emulates human vision systems

Unstructured light fields

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 35-38).We present a system for interactively acquiring and rendering light fields using a hand-held commodity camera. The main challenge we address is assisting a user in achieving good coverage of the 4D domain despite the challenges of hand-held acquisition. We define coverage by bounding reprojection error between viewpoints, which accounts for all 4 dimensions of the light field. We use this criterion together with a recent Simultaneous Localization and Mapping technique to compute a coverage map on the space of viewpoints. We provide users with real-time feedback and direct them toward under-sampled parts of the light field. Our system is lightweight and has allowed us to capture hundreds of light fields. We further present a new rendering algorithm that is tailored to the unstructured yet dense data we capture. Our method can achieve piecewise-bicubic reconstruction using a triangulation of the captured viewpoints and subdivision rules applied to reconstruction weights.by Myers Abraham Davis (Abe Davis).S.M

The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack

Post capture refocusing effect in smartphone cameras is achievable by using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the extended depth of field effect in this application can be improved significantly by computing an accurate depth map which has been an open issue for decades. To tackle this issue, in this paper, a framework is proposed based on Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth from the focal stack and synthetic defocus application. In addition to its ability to provide high structural accuracy and occlusion handling, the optimization function of the proposed method can, in fact, converge faster and better than state of the art methods. The evaluation has been done on 21 sets of focal stacks and the optimization function has been compared against 5 other methods. Preliminary results indicate that the proposed method has a better performance in terms of structural accuracy and optimization in comparison to the current state of the art methods.Comment: 15 pages, 8 figure

The standard plenoptic camera: applications of a geometrical light field model

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyThe plenoptic camera is an emerging technology in computer vision able to capture a light field image from a single exposure which allows a computational change of the perspective view just as the optical focus, known as refocusing. Until now there was no general method to pinpoint object planes that have been brought to focus or stereo baselines of perspective views posed by a plenoptic camera. Previous research has presented simplified ray models to prove the concept of refocusing and to enhance image and depth map qualities, but lacked promising distance estimates and an efficient refocusing hardware implementation. In this thesis, a pair of light rays is treated as a system of linear functions whose solution yields ray intersections indicating distances to refocused object planes or positions of virtual cameras that project perspective views. A refocusing image synthesis is derived from the proposed ray model and further developed to an array of switch-controlled semi-systolic FIR convolution filters. Their real-time performance is verified through simulation and implementation by means of an FPGA using VHDL programming. A series of experiments is carried out with different lenses and focus settings, where prediction results are compared with those of a real ray simulation tool and processed light field photographs for which a blur metric has been considered. Predictions accurately match measurements in light field photographs and signify deviations of less than 0.35 % in real ray simulation. A benchmark assessment of the proposed refocusing hardware implementation suggests a computation time speed-up of 99.91 % in comparison with a state-of-the-art technique. It is expected that this research supports in the prototyping stage of plenoptic cameras and microscopes as it helps specifying depth sampling planes, thus localising objects and provides a power-efficient refocusing hardware design for full-video applications as in broadcasting or motion picture arts

Consideraciones acerca de la viabilidad de un sensor plenóptico en dispositivos de consumo

Doctorado en Ingeniería IndustrialPassive distance measurement of the objects in an image gives place to interesting applications that have the potential to revolutionize the field of photography. In this thesis a prototype of plenoptic camera for mobile devices was created and studied. This technique has two main disadvantages: the need for modifying the camera module and the loss of resolution. Because of this, the prototype was discarded in order to utilize another technique: depth from focus. In this technique the capture method consists in taking several images while varying the focus distance. The set of images is called focal-stack. Different focus operators are studied, which give a measure of defocus per pixel and plane of the focal-stack. The curvelet based focus operator is chosen as the most adequate. It is computationally more intensive than other operators but it is capable of decomposing natural images using few coefficients. In order to make viable its usage in mobile devices a new curvelet transform based on the discrete Radon transform is built. The discrete Radon transform has logarithmic complexity, does not use the Fourier transform and uses only integer sums. Lastly, different versions of the Radon transform are analyzed with the goal of achieving an even faster transform. These transforms are implemented to be executed on mobile devices. Additionally, an application of the Radon transform is presented. It consists in the detection of bar-codes that have any orientation in an image.La medida pasiva de distancia a los objetos en una imagen da lugar a interesantes aplicaciones con capacidad para revolucionar la fotografía. En esta tesis se creó y estudió un prototipo de cámara plenóptica para dispositivos móviles. Esta técnica presenta dos inconvenientes: la necesidad de modificar el módulo de cámara y la pérdida de resolución. Por ello, el prototipo fue descartado para utilizar otra técnica: la profundidad a partir del desenfoque. En esta técnica el método de captura consiste en tomar varias imagenes variando la distancia de enfoque. El conjunto de imágenes se denomina focal-stack. Se estudian distintos operadores de desenfoque, que dan una medida de desenfoque por pixel y por plano del focal-stack. Siendo elegido como óptimo el operador de desenfoque curvelet, que es computacionalmente más intensivo que otros operadores pero es capaz de descomponer imagenes naturales utilizando muy pocos coeficientes. Para hacer posible su uso en dispositivos móviles se construye una nueva transformada curvelet basada en la transformada discreta de Radon. La transformada discreta de Radon tiene complejidad linearítmica, no utiliza la transformada de Fourier y usa sólo sumas de enteros. Por último, se analizan distintas versiones de la transformada de Radon con el objetivo de conseguir una transformada aún más rápida y se implementan para ser ejecutadas en dispositivos móviles. Además se presenta una aplicación de la transformada de Radon consistente en la detección de códigos de barras con cualquier orientación en una imagen

From Calibration to Large-Scale Structure from Motion with Light Fields

Classic pinhole cameras project the multi-dimensional information of the light flowing through a scene onto a single 2D snapshot. This projection limits the information that can be reconstructed from the 2D acquisition. Plenoptic (or light field) cameras, on the other hand, capture a 4D slice of the plenoptic function, termed the “light field”. These cameras provide both spatial and angular information on the light flowing through a scene; multiple views are captured in a single photographic exposure facilitating various applications. This thesis is concerned with the modelling of light field (or plenoptic) cameras and the development of structure from motion pipelines using such cameras. Specifically, we develop a geometric model for a multi-focus plenoptic camera, followed by a complete pipeline for the calibration of the suggested model. Given a calibrated light field camera, we then remap the captured light field to a grid of pinhole images. We use these images to obtain metric 3D reconstruction through a novel framework for structure from motion with light fields. Finally, we suggest a linear and efficient approach for absolute pose estimation for light fields