Depth estimation and image restoration using defocused stereo pairs

Rajagopalan, A. N.; Chaudhuri, Subhasis; Mudenagudi, Uma

doi:10.1109/tpami.2004.102

Cited by 139 publications

(60 citation statements)

References 19 publications

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“…We select samples equally spaced over the domain with a relatively small interval, say ∆r = 0.1. Then a set of sample PSFs can be generated according to the PSF model, and their blur spectrum {σ 2 i (r)} can be calculated by equation (6) for each basis function t i . We note that these discrete samples are only used to generate the continuous fitting functions {σ Once the function set {σ 2 i (r)} i is available, given a fixed s, the optimalr can be generated by maximizing function (5), or equivalently by minimizing the following: (13) can be optimized through a gradient descent algorithm.…”

Section: A Local Probability Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Spatially Varying Defocus Blur From A Single Image

Zhu

Cohen

Schiller

et al. 2013

IEEE Trans. on Image Process.

139

111

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Abstract-Estimating the amount of blur in a given image is important for computer vision applications. More specifically, the spatially varying defocus point-spread-functions (PSFs) over an image reveal geometric information of the scene, and their estimate can also be used to recover an all-in-focus image. A PSF for a defocus blur can be specified by a single parameter indicating its scale. Most existing algorithms can only select an optimal blur from a finite set of candidate PSFs for each pixel. Some of those methods require a coded aperture filter inserted in the camera. In this paper we present an algorithm estimating a defocus scale map from a single image, which is applicable to conventional cameras. This method is capable of measuring the probability of local defocus scale in the continuous domain. It also takes smoothness and color edge information into consideration to generate a coherent blur map indicating the amount of blur at each pixel. Simulated and real data experiments illustrate excellent performance, and its successful applications in foreground/background segmentation.Index Terms-spatially varying blur estimation, defocus blur.

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Section: A Local Probability Estimationmentioning

confidence: 99%

“…The computation of depth information typically requires two photos of the same scene taken at the same time but from slightly different vantage points, i.e. a stereo pair [6]. However, in most cases only one image is available.…”

Section: Introductionmentioning

confidence: 99%

Estimating Spatially Varying Defocus Blur From A Single Image

Zhu

Cohen

Schiller

et al. 2013

IEEE Trans. on Image Process.

139

111

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show abstract

“…Closely related to our work are [1,13,16], which strongly couple blur and motion. The work in [13] uses latarally translated binocular stereo while that in [16] involves axial camera translation with no intrinsic parameter variations.…”

Section: Related Workmentioning

confidence: 79%

Depth Estimation and Inpainting with an Unconstrained Camera

Bhavsar

Rajagopalan

2010

Procedings of the British Machine Vision Conference 2010

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Unrestricted camera motion and the ability to operate over a range of lens parameters are often desirable when using an off-the-shelf camera. Variations in intrinsic and extrinsic parameters induce defocus and pixel motion, both of which relate to scene structure. We propose a depth estimation approach by elegantly coupling the motion and defocus cues. We further advocate a natural extension of our framework for inpainting both depth and image, using the motion cue. Unlike traditional inpainting, our approach also considers defocus blur. This ensures that the image inpainting is coherent with respect to defocus. We use the belief propagation method in our estimation approach, which also handles occlusions and uses the color image segmentation cue.

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“…For example, Rajagopalan et al used 4 images based on the combinations of 2 viewpoints and 2 focus distances, and applied conventional stereo and DFD algorithms to the pairs of same focus distances and viewpoints, respectively [10]. Therefore, by their method, it is impossible to handle 2 images with both disparity and different focus distance.…”

Section: Related Workmentioning

confidence: 99%