Single-lens single-image incoherent passive-ranging systems

Er, Dowski; Wt, Cathey

doi:10.1364/ao.33.006762

Cited by 49 publications

(24 citation statements)

References 8 publications

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“…9 For instance, the PSF shown in Fig. 1 is a superposition of modes with indices ͑m , n͒ equal to (1,1), (3,5), (5,9), (7,13), and (9,17), where the indices are defined in Ref. 9.…”

Section: ͑1͒mentioning

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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“…9 For instance, the PSF shown in Fig. 1 is a superposition of modes with indices ͑m , n͒ equal to (1,1), (3,5), (5,9), (7,13), and (9,17), where the indices are defined in Ref. 9.…”

Section: ͑1͒mentioning

confidence: 99%

“…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%

Depth from diffracted rotation

2006

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“…The key idea is to use aspheric lenses to render the lens point spread function (PSF) depth-invariant. Then, shift-invariant deblurring with a fixed known blur can be applied to sharpen the image [19,20]. However, while the results are quite promising, the PSF is not fully depth-invariant and artifacts are still present in the reconstructed image.…”

Section: Depth-invariant Blurmentioning

confidence: 99%

“…As pointed out in [19] we identify two main challenges: The first one is that accurate deblurring requires accurate identification of the blur scale; the second one is that accurate deblurring requires little texture loss due to blurring. A first step towards addressing these challenges is to define a metric for blur scale identification and a metric for texture loss.…”

Section: Coded Aperture Selectionmentioning

confidence: 99%

Single Image Blind Deconvolution with Higher-Order Texture Statistics

Martinello

Favaro

2011

Video Processing and Computational Video

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Abstract. We present a novel method for solving blind deconvolution, i.e., the task of recovering a sharp image given a blurry one. We focus on blurry images obtained from a coded aperture camera, where both the camera and the scene are static, and allow blur to vary across the image domain. As most methods for blind deconvolution, we solve the problem in two steps: First, we estimate the coded blur scale at each pixel; second, we deconvolve the blurry image given the estimated blur. Our approach is to use linear high-order priors for texture and second-order priors for the blur scale map, i.e., constraints involving two pixels at a time. We show that by incorporating the texture priors in a least-squares energy minimization we can transform the initial blind deconvolution task in a simpler optimization problem. One of the striking features of the simplified optimization problem is that the parameters that define the functional can be learned offline directly from natural images via singular value decomposition. We also show a geometrical interpretation of image blurring and explain our method from this viewpoint. In doing so we devise a novel technique to design optimally coded apertures. Finally, our coded blur identification results in computing convolutions, rather than deconvolutions, which are stable operations. We will demonstrate in several experiments that this additional stability allows the method to deal with large blur. We also compare our method to existing algorithms in the literature and show that we achieve state-of-the-art performance with both synthetic and real data.

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“…The phase plate controls the wavefront of rays according to the positions in aperture open. Dowski et al [1] proposed a phase plate for DFD estimation whose PSF spectrum has many zero-crossings. Levin et al [6] theoretically analyzed the upper bound of the PSF response for image deblurring and designed optics called a lattice focus lens to realize the PSF.…”

Section: Related Workmentioning

confidence: 99%

Half-Sweep Imaging for Depth from Defocus

Matsui

Nagahara

Taniguchi

2011

Advances in Image and Video Technology

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Abstract. Depth from defocus (DFD) is a technique to recover the scene depth from defocusing in images. DFD usually involves two differently focused images (near-focused and far-focused) and calculates the size of the depth blur in the captured images. In recent years, the coded aperture technique, which uses a special pattern for the aperture to engineer the point spread function (PSF), has been used to improve the accuracy of DFD estimation. However, coded aperture sacrifices an incident light and loses a SNR of captured images which is needed for the accurate estimation. In this paper, we propose a new computational imaging, called half-sweep imaging. Half-sweep imaging engineers PSFs for improving DFD and maintaining the SNR of captured images. We confirmed the advantage of the imaging in comparison with conventional DFD and coded aperture in experiments.

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Single-lens single-image incoherent passive-ranging systems

Cited by 49 publications

References 8 publications

Depth from diffracted rotation

Depth from diffracted rotation

Single Image Blind Deconvolution with Higher-Order Texture Statistics

Half-Sweep Imaging for Depth from Defocus

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