Traditional shape-from-focus (SFF) uses focus as the singular cue to derive the shape profile of a 3D object from a sequence of images. However, the stack of low-resolution (LR) observations is space-variantly blurred because of the finite depth of field of the camera. The authors propose to exploit the defocus information in the stack of LR images to obtain a super-resolved image as well as a high-resolution (HR) depth map of the underlying 3D object. Appropriate observation models are used to describe the image formation process in SFF. Local spatial dependencies of the intensities of pixels and their depth values are accounted for by modelling the HR image and the HR structure as independent Markov random fields. Taking as input the LR images from the stack and the LR depth map, the authors first obtain the super-resolved image of the 3D specimen and use it subsequently to reconstruct a HR depth profile of the object. © 2008 The Institution of Engineering and Technology.

A. N. Rajagopalan

Department of Electrical Engineering

Computer networks

ELECTRIC REACTORS

ENGINEERING TECHNOLOGY

Hidden Markov models

Image acquisition

image enhancement

Imaging techniques

Maps

Photography

Special effects

Technology

(OTDR) TECHNOLOGY

Defocus

defocus blur

DEPTH MAPS

Depth profiling

FINITE DEPTH

HIGH RESOLUTION INFORMATION

High resolutions

IMAGE FORMATIONS

LOW RESOLUTION (LR)

LOW-RESOLUTION (LR) IMAGES

Markov random field (MRF)

OBSERVATION MODELS

SHAPE FROM FOCUS (SFF)

three dimensional

Fulltext

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IIT Madras

IET Computer Vision

In this paper, we extend traditional Shape from focus (SFF) to the underwater scenario. Specifically, we show how 3D shape of objects immersed in water can be extracted from images captured under an optical microscope. Traditional SFF employs telecentric optics to achieve geometric registration among the frames. We extend conventional SFF to underwater objects under the assumption of negligible scattering. We establish that the property of telecentricity holds for objects immersed in water provided the numerical aperture is small. By modeling geometrical distortions due to refraction effects on the water surface, we prove that the depth map obtained in the presence of water is a scaled version of the original depth map. We also reveal that this scale factor is directly related to the refractive index of water. We validate performance with real experiments. © 2014 IEEE.

Proceedings - International Conference on Pattern Recognition

Underwater microscopic shape from focus

Traditional depth estimation methods typically exploit the effect of either the variations in internal parameters such as aperture and focus (as in depth from defocus), or variations in extrinsic parameters such as position and orientation of the camera (as in stereo).When operating off-the-shelf (OTS) cameras in a general setting, these parameters influence the depth of field (DOF) and field of view (FOV). While DOF mandates one to deal with defocus blur, a larger FOV necessitates camera motion during image acquisition. As a result, for unfettered operation of an OTS camera, it becomes inevitable to account for pixel motion as well as optical defocus blur in the captured images. We propose a depth estimation framework using calibrated images captured under general camera motion and lens parameter variations. Our formulation seeks to generalize the constrained areas of stereo and shape from defocus (SFD)/focus (SFF) by handling, in tandem, various effects such as focus variation, zoom, parallax and stereo occlusions, all under one roof. One of the associated challenges in such an unrestrained scenario is the problem of removing user-defined foreground occluders in the reference depth map and image (termed inpainting of depth and image). Inpainting is achieved by exploiting the cue from motion parallax to discover (in other images) the correspondence/color information missing in the reference image. Moreover, considering the fact that the observations could be differently blurred, it is important to ensure that the degree of defocus in the missing regions (in the reference image) is coherent with the local neighbours (defocus inpainting). © 2011 Springer Science+Business Media, LLC.

International Journal of Computer Vision

Towards unrestrained depth inference with coherent occlusion filling

Shape from focus is an elegant method that estimates the structure of a 3D object from a video of captured frames using the degree of focus as the principal cue. However, the quality of the estimated structure is vulnerable to scene texture. The effect is particularly pronounced for objects that are smooth relative to the magnification of the optical system. In this paper, the shape estimation process is cast as an inverse problem. We exploit spatial dependencies by modeling the shape of the object with a discontinuity-adaptive Markov random field wherein the focus measure profile is used to judiciously control the degree of smoothness. The 3D information is obtained by minimizing a suitably derived energy function that preserves fine details of the underlying structure. We show by experimentation on several real-world specimens that our method yields state-of-the-art performance. © 2011 The Author Journal of Microscopy © 2011 Royal Microscopical Society.

Journal of Microscopy

Shape extraction of low-textured objects in video microscopy

We propose a new method that extends the capability of shape-from-focus (SFF) to estimate the depth profile of 3-D objects in the presence of structure-dependent pixel motion. Existing SFF techniques work under the constraint that there is no parallax in the captured stack of frames. However, in off-the-shelf cameras, there can be appreciable pixel motion among the observations when there is relative motion between the object and the camera. In such a scenario, the depth estimates will be erroneous if the parallax effect is not factored in. Our degradation model accounts for pixel migration effects in the observations due to parallax resulting in a generalization of the SFF technique. We show that pixel motion and defocus blur therein are tightly coupled to the underlying shape of the 3-D object. Simultaneous reconstruction of the underlying 3-D structure and the all-in-focus image is carried out within an optimization framework using local image operations. The proposed method when tested on many examples, both synthetic and real, is very effective and delivers state-of-the-art performance. © 2011 IEEE.

IEEE Transactions on Image Processing

Dealing with parallax in shape-from-focus

Harnessing defocus blur to recover high-resolution information in shape-from-focus technique

2007 IEEE Conference on Computer Vision and Pattern Recognition

Spatial-Depth Super Resolution for Range Images

Signal Processing: Image Communication

Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability

EURASIP Journal on Advances in Signal Processing

A Fast Algorithm for Image Super-Resolution from Blurred Observations

We present a super-resolution technique where the 3-D shape preservation is used as a constraint while super-resolving a scene. Given the observations under different illuminant positions, we combine these observations to obtain the super-resolved image and the spatially enhanced scene structure simultaneously. The use of shape cue in the form of photometric measurements, instead of the motion cue, eliminates the need for image registration with a sub-pixel accuracy. We model the high-resolution image, the structure, and the albedo of the surface as separate Markov random fields and super-resolve them using a suitable regularization scheme. Results of the experimentation on real data are presented. © 2005 Elsevier Inc. All rights reserved.

Computer Vision and Image Understanding

Simultaneous estimation of super-resolved depth map and intensity field using photometric cue

Journal	IET Computer Vision
ISSN	17519632
Open Access	Yes