An investigation of methods for determining depth from focus

Ens, J.; Lawrence, P.D.

doi:10.1109/34.192482

Cited by 304 publications

(143 citation statements)

References 16 publications

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“…Two images are needed to make the DFD estimation well posed. [1][2][3][4][5][6][7]14,17 Hence in addition to i rot , we acquire a reference frame, i ref .…”

Section: ͑1͒mentioning

confidence: 99%

“…Typical systems have utilized a clear, circular aperture as is found in standard camera lenses. [1][2][3][4][5][6][7] However, the point-spread function (PSF) of such systems has not been optimized for depth estimation. Therefore in this Letter we engineer the PSF to achieve enhanced performance in this specific task.…”

mentioning

confidence: 99%

“…This principle is exploited in techniques known as depth from defocus (DFD) by jointly processing frames acquired in different focus or aperture settings. [1][2][3][4][5][6][7] Relative to stereovision, DFD is more robust to occlusion and correspondence problems. 8 Moreover, in applications that require a large numerical aperture (NA), particularly in high-magnification microscopy, DFD is more suitable than stereovision.…”

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confidence: 99%

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Depth from diffracted rotation

2006

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The accuracy of depth estimation based on defocus effects has been essentially limited by the depth of field of the imaging system. We show that depth estimation can be improved significantly relative to classical methods by exploiting three-dimensional diffraction effects. We formulate the problem by using information theory analysis and present, to the best of our knowledge, a new paradigm for depth estimation based on spatially rotating point-spread functions (PSFs). Such PSFs are fundamentally more sensitive to defocus thanks to their first-order axial variation. Our system acquires a frame by using a rotating PSF and jointly processes it with an image acquired by using a standard PSF to recover depth information. Analytical, numerical, and experimental evidence suggest that the approach is suitable for applications such as microscopy and machine vision. © 2006 Optical Society of America OCIS codes: 110.6880, 110.4850, 100.6640, 150.5670. The human visual system uses defocus as a depth cue. 1 Optical images convey three-dimensional (3D) information by the amount of blur in each image region: the further the object is from the in-focus plane, the more blurred it appears. This principle is exploited in techniques known as depth from defocus (DFD) by jointly processing frames acquired in different focus or aperture settings. [1][2][3][4][5][6][7] Relative to stereovision, DFD is more robust to occlusion and correspondence problems. 8 Moreover, in applications that require a large numerical aperture (NA), particularly in high-magnification microscopy, DFD is more suitable than stereovision. Previous DFD work has concentrated on the implementation of signal processing algorithms based on a geometrical optical model. Typical systems have utilized a clear, circular aperture as is found in standard camera lenses. 1-7 However, the point-spread function (PSF) of such systems has not been optimized for depth estimation. Therefore in this Letter we engineer the PSF to achieve enhanced performance in this specific task. We exploit the freedom provided by diffractive optics to design unconventional optical responses. In particular, we investigate 3D PSFs whose transverse cross sections rotate with respect to each other as a result of diffraction in free space. 9-14 Rotating PSFs provide a faster rate of change with depth than PSFs of clear pupil systems having the same NA. 9 As a consequence, we show here that rotating PSFs present approximately an order of magnitude increase in Fisher information (FI) along the depth dimension, when compared with standard pupils. Finally, we demonstrate this principle in an experiment based on a two-channel system that encodes a rotating PSF.The more dissimilar the PSF is at different values of defocus, the easier it is to distinguish between depth planes in the presence of noise. Defocus is typically quantified by the defocus parameter , defined as 15where is the wavelength of light, and z obj focus and z obj Ј are the in-focus and actual object distances from the entrance pupil, re...

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“…Two images are needed to make the DFD estimation well posed. [1][2][3][4][5][6][7]14,17 Hence in addition to i rot , we acquire a reference frame, i ref .…”

Section: ͑1͒mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Depth from diffracted rotation

2006

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“…In local DFD, a window around every pixel point is predefined, and the point's blurring is defined as that of the window [7,13]. However, the difficulty in selecting proper size of window is a well-known disadvantage of DFD algorithm, because there is a trade-off between having a window that is as large as possible to average out noise, but as small as possible to guarantee that within it [14,15]. As far as global DFD is concerned, its main idea is completely different from the local DFD algorithm, since it works on the entire image without information of its radiance, or the appearance of the surfaces, and depth.…”

Section: Introductionmentioning

confidence: 99%

“…As far as global DFD is concerned, its main idea is completely different from the local DFD algorithm, since it works on the entire image without information of its radiance, or the appearance of the surfaces, and depth. Therefore it is necessary to construct the depth model and the radiance model simultaneously [14,[16][17][18]. This, however, will bring the problem of huge computation cost.…”

Section: Introductionmentioning

confidence: 99%

Depth measurement using single camera with fixed camera parameters

Wei

Dong

2012

IET Comput. Vis.

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Owing to the space limitation and the strict requirement on operation, depth measurement using single visual sensor is necessary in many applications, such as mini-robot, precision processing and micro/nano-manipulation. Depth from defocus (DFD), a typical method applied in depth reconstruction, has been extensively researched and has developed greatly in recent years. However, all the existing DFD algorithms has focused only on the situation that blurring images with different camera parameters (i.e. focal length or radius of the lens), and it resulted in the inapplicability of these algorithms in cases where any change of camera parameters is absolutely forbidden. Therefore a novel DFD method considering different images with fixed camera parameters is given. First, the blurring imaging model is constructed with the relative blurring and the diffusion equation. Secondly the relation between depth and blurring is discussed. Subsequently, the depth measurement problem is transformed into an optimisation issue. Finally, simulations and experiments are conducted to show the feasibility and effectiveness of the proposed method.

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