Review of image-blur models in a photographic system using the principles of optics

Lee, Hsien-Che

doi:10.1117/12.55609

Cited by 54 publications

(28 citation statements)

References 24 publications

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“…We use the pillbox point spread function (PSF) model [11,12,21], since it is valid for aberration-free geometric optics, and has been shown to be a good approximation for large defocus [7,10,20]. In this model…”

Section: Error Propagationmentioning

confidence: 99%

The optimal axial interval in estimating depth from defocus

Schechner

Kiryati

1999

Proceedings of the Seventh IEEE International Conference on Computer Vision

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Section: Error Propagationmentioning

confidence: 99%

The optimal axial interval in estimating depth from defocus

Schechner

Kiryati

1999

Proceedings of the Seventh IEEE International Conference on Computer Vision

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“…The blurring effect can be thought of as a convolution between the perfectly focused image and a blurring function called point spread function ͑PSF͒. In vision literature various models have been proposed to approximate the blurring function 10,23,24 but in practice the two-dimensional Gaussian 1,9,25 has been widely employed to approximate the PSF when paraxial geometric optics are used and diffraction effects are negligible.…”

Section: The Blur Functionmentioning

confidence: 99%

Computational approach for depth from defocus

Ghita

Whelan

Mallon

2005

J. Electron. Imaging

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Abstract. Active depth from defocus (DFD) eliminates the main limitation faced by passive DFD, namely its inability to recover depth when dealing with scenes defined by weakly textured (or textureless) objects. This is achieved by projecting a dense illumination pattern onto the scene and depth can be recovered by measuring the local blurring of the projected pattern. Since the illumination pattern forces a strong dominant texture on imaged surfaces, the level of blurring is determined by applying a local operator (tuned on the frequency derived from the illumination pattern) as opposed to the case of window-based passive DFD where a large range of band pass operators are required. The choice of the local operator is a key issue in achieving precise and dense depth estimation. Consequently, in this paper we introduce a new focus operator and we propose refinements to compensate for the problems associated with a suboptimal local operator and a nonoptimized illumination pattern. The developed range sensor has been tested on real images and the results demonstrate that the performance of our range sensor compares well with those achieved by other implementations, where precise and computationally expensive optimization techniques are employed.

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“…Jérôme Da Rugna and Hubert Konik are with the Laboratoire LIGIV, Université Jean Monnet, 18 rue B. Lauras, 42000 Saint-Étienne, France (email: {darugna,konik}@ligiv.org) 1 The symbol ⋆ represents the convolution operator…”

Section: A Point Spread Functionmentioning

confidence: 99%

“…with x a pixel, g the blurred image, f the unknown sharp image and h the point spread function, noted PSF [1] 1 . In the case of image restoration, the blur is unwanted and has to be compensated.…”

Section: A Point Spread Functionmentioning

confidence: 99%

Blur identification in image processing

Rugna

Konik

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Abstract-The aim of this study is to achieve a blur identification task in still images. In fact, in photographic camera, the optical lenses may be set in a way to clearly distinct two areas in the image : the blurry one and the non blurry one. An automatic segmentation coupled to specific descriptors allow first to describe any region of the image. Then, a supervised learning processes permits to build a classifier able to decide for each unknown region the label "Blurry" or "Sharp". We discuss here precisely the overall process, from the objective choice of the segmentation algorithm to the presentation of the different introduced descriptors. Finally, some results are presented validating such an approach.

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Review of image-blur models in a photographic system using the principles of optics

Cited by 54 publications

References 24 publications

The optimal axial interval in estimating depth from defocus

The optimal axial interval in estimating depth from defocus

Computational approach for depth from defocus

Blur identification in image processing

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