Aperture Supervision for Monocular Depth Estimation

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image.Comment: To appear at CVPR 2018 (updated to camera ready version

Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions

Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features

Single image defocus estimation by modified gaussian function

© 2019 John Wiley & Sons, Ltd. This article presents an algorithm to estimate the defocus blur from a single image. Most of the existing methods estimate the defocus blur at edge locations, which further involves the reblurring process. For this purpose, existing methods use the traditional Gaussian function in the phase of reblurring but it is found that the traditional Gaussian kernel is sensitive to the edges and can cause loss of edges information. Hence, there are more chances of missing spatially varying blur at edge locations. We offer the repeated averaging filters as an alternative to the traditional Gaussian function, which is more effective, and estimate the spatially varying defocus blur at edge locations. By using repeated averaging filters, a blur sparse map is computed. The obtained sparse map is propagated by integration of superpixels segmentation and transductive inference to estimate full defocus blur map. Our adopted method of repeated averaging filters has less computational time of defocus blur map estimation and has better visual estimates of the final defocus recovered map. Moreover, it has surpassed many previous state-of-the-art proposed systems in terms of quantative analysis

Determining the Phase and Amplitude Distortion of a Wavefront using a Plenoptic Sensor

We have designed a plenoptic sensor to retrieve phase and amplitude changes resulting from a laser beam's propagation through atmospheric turbulence. Compared with the commonly restricted domain of (-pi, pi) in phase reconstruction by interferometers, the reconstructed phase obtained by the plenoptic sensors can be continuous up to a multiple of 2pi. When compared with conventional Shack-Hartmann sensors, ambiguities caused by interference or low intensity, such as branch points and branch cuts, are less likely to happen and can be adaptively avoided by our reconstruction algorithm. In the design of our plenoptic sensor, we modified the fundamental structure of a light field camera into a mini Keplerian telescope array by accurately cascading the back focal plane of its object lens with a microlens array's front focal plane and matching the numerical aperture of both components. Unlike light field cameras designed for incoherent imaging purposes, our plenoptic sensor operates on the complex amplitude of the incident beam and distributes it into a matrix of images that are simpler and less subject to interference than a global image of the beam. Then, with the proposed reconstruction algorithms, the plenoptic sensor is able to reconstruct the wavefront and a phase screen at an appropriate depth in the field that causes the equivalent distortion on the beam. The reconstructed results can be used to guide adaptive optics systems in directing beam propagation through atmospheric turbulence. In this paper we will show the theoretical analysis and experimental results obtained with the plenoptic sensor and its reconstruction algorithms.Comment: This article has been accepted by JOSA

Three dimensional moving pictures with a single imager and microfluidic lens

Three-dimensional movie acquisition and corresponding depth data is commonly generated from multiple cameras and multiple views. This technology has high cost and large size which are limitations for medical devices, military surveillance and current consumer products such as small camcorders and cell phone movie cameras. This research result shows that a single imager, equipped with a fast-focus microfluidic lens, produces a highly accurate depth map. On test material, the depth is found to be an average Root Mean Squared Error (RMSE) of 3.543 gray level steps (1.38\%) accuracy compared to ranging data. The depth is inferred using a new Extended Depth from Defocus (EDfD), and defocus is achieved at movie speeds with a microfluidic lens. Camera non-uniformities from both lens and sensor pipeline are analysed. The findings of some lens effects can be compensated for, but noise has the detrimental effect. In addition, early indications show that real-time HDTV 3D movie frame rates are feasible

The Optical Design of CHARIS: An Exoplanet IFS for the Subaru Telescope

High-contrast imaging techniques now make possible both imaging and spectroscopy of planets around nearby stars. We present the optical design for the Coronagraphic High Angular Resolution Imaging Spectrograph (CHARIS), a lenslet-based, cryogenic integral field spectrograph (IFS) for imaging exoplanets on the Subaru telescope. The IFS will provide spectral information for 138x138 spatial elements over a 2.07 arcsec x 2.07 arcsec field of view (FOV). CHARIS will operate in the near infrared (lambda = 1.15 - 2.5 microns) and will feature two spectral resolution modes of R = 18 (low-res mode) and R = 73 (high-res mode). Taking advantage of the Subaru telescope adaptive optics systems and coronagraphs (AO188 and SCExAO), CHARIS will provide sufficient contrast to obtain spectra of young self-luminous Jupiter-mass exoplanets. CHARIS will undergo CDR in October 2013 and is projected to have first light by the end of 2015. We report here on the current optical design of CHARIS and its unique innovations.Comment: 15 page

Modeling and applications of the focus cue in conventional digital cameras

El enfoque en cámaras digitales juega un papel fundamental tanto en la calidad de la imagen como en la percepción del entorno. Esta tesis estudia el enfoque en cámaras digitales convencionales, tales como cámaras de móviles, fotográficas, webcams y similares. Una revisión rigurosa de los conceptos teóricos detras del enfoque en cámaras convencionales muestra que, a pasar de su utilidad, el modelo clásico del thin lens presenta muchas limitaciones para aplicación en diferentes problemas relacionados con el foco. En esta tesis, el focus profile es propuesto como una alternativa a conceptos clásicos como la profundidad de campo. Los nuevos conceptos introducidos en esta tesis son aplicados a diferentes problemas relacionados con el foco, tales como la adquisición eficiente de imágenes, estimación de profundidad, integración de elementos perceptuales y fusión de imágenes. Los resultados experimentales muestran la aplicación exitosa de los modelos propuestos.The focus of digital cameras plays a fundamental role in both the quality of the acquired images and the perception of the imaged scene. This thesis studies the focus cue in conventional cameras with focus control, such as cellphone cameras, photography cameras, webcams and the like. A deep review of the theoretical concepts behind focus in conventional cameras reveals that, despite its usefulness, the widely known thin lens model has several limitations for solving different focus-related problems in computer vision. In order to overcome these limitations, the focus profile model is introduced as an alternative to classic concepts, such as the near and far limits of the depth-of-field. The new concepts introduced in this dissertation are exploited for solving diverse focus-related problems, such as efficient image capture, depth estimation, visual cue integration and image fusion. The results obtained through an exhaustive experimental validation demonstrate the applicability of the proposed models

Depth Acquisition from Digital Images

Introduction: Depth acquisition from digital images captured with a conventional camera, by analysing focus/defocus cues which are related to depth via an optical model of the camera, is a popular approach to depth-mapping a 3D scene. The majority of methods analyse the neighbourhood of a point in an image to infer its depth, which has disadvantages. A more elegant, but more difficult, solution is to evaluate only the single pixel displaying a point in order to infer its depth. This thesis investigates if a per-pixel method can be implemented without compromising accuracy and generality compared to window-based methods, whilst minimising the number of input images. Method: A geometric optical model of the camera was used to predict the relationship between focus/defocus and intensity at a pixel. Using input images with different focus settings, the relationship was used to identify the focal plane depth (i.e. focus setting) where a point is in best focus, from which the depth of the point can be resolved if camera parameters are known. Two metrics were implemented, one to identify the best focus setting for a point from the discrete input set, and one to fit a model to the input data to estimate the depth of perfect focus of the point on a continuous scale. Results: The method gave generally accurate results for a simple synthetic test scene, with a relatively low number of input images compared to similar methods. When tested on a more complex scene, the method achieved its objectives of separating complex objects from the background by depth, and produced a similar resolution of a complex 3D surface as a similar method which used significantly more input data. Conclusions: The method demonstrates that it is possible to resolve depth on a per-pixel basis without compromising accuracy and generality, and using a similar amount of input data, compared to more traditional window-based methods. In practice, the presented method offers a convenient new option for depth-based image processing applications, as the depth-map is per-pixel, but the process of capturing and preparing images for the method is not too practically cumbersome and could be easily automated unlike other per-pixel methods reviewed. However, the method still suffers from the general limitations of the depth acquisition approach using images from a conventional camera, which limits its use as a general depth acquisition solution beyond specifically depth-based image processing applications