&lt;title&gt;Parametric PSF estimation via sparseness maximization in the wavelet domain&lt;/title&gt;

Rooms, Filip; Philips, Wilfried; Portilla, Javier

doi:10.1117/12.571539

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…This is justified by the observation that most of the degradations decrease the sparseness of the representation (e.g., wavelets) with respect to the original image [120,121,24]. Figure 5.1 shows an example.…”

Section: Chapter 5 Application To Image Restorationmentioning

confidence: 99%

Artificial Intelligence

Francis¹

2010

Encyclopedia of Nanoscience and Society

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Section: Chapter 5 Application To Image Restorationmentioning

confidence: 99%

Artificial Intelligence

Francis¹

2010

Encyclopedia of Nanoscience and Society

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“…4). Other blind approaches try to estimate the PSF based on statistical models of sharp images (Chalmond 1991;Rooms et al 2004;Zhang and Cham 2008;Šroubek et al 2007). Since the blind estimation is an ill-posed problem (blind source separation), strong kernel smoothness assumptions or, equivalently, very simple parametric models are necessary.…”

Section: Introductionmentioning

confidence: 99%

The Non-parametric Sub-pixel Local Point Spread Function Estimation Is a Well Posed Problem

Delbracio

Musé²,

Almansa³

et al. 2011

Int J Comput Vis

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Most medium to high quality digital cameras (DSLRs) acquire images at a spatial rate which is several times below the ideal Nyquist rate. For this reason only aliased versions of the cameral point-spread function (PSF) can be directly observed. Yet, it can be recovered, at a sub-pixel resolution, by a numerical method. Since the acquisition system is only locally stationary, this PSF estimation must be local. This paper presents a theoretical study proving that the sub-pixel PSF estimation problem is well-posed even with a single well chosen observation. Indeed, theoretical bounds show that a near-optimal accuracy can be achieved with a calibration pattern mimicking a Bernoulli(0.5) random noise. The physical realization of this PSF estimation method is demonstrated in many comparative experiments. We use an algorithm to accurately estimate the pattern position and its illumination conditions. Once this accurate registration is obtained, the local PSF can be directly computed by inverting a well conditioned linear system. The PSF estimates reach stringent accuracy levels M. Delbracio ( ) · J.-M. Morel CMLA, ENS de Cachan, with a relative error of the order of 2% to 5%. To the best of our knowledge, such a regularization-free and model-free sub-pixel PSF estimation scheme is the first of its kind.

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“…For instance, Single-channel blind deconvolution within Bayesian framework is considered in [18]. The parameter for Gaussian blur is determined efficiently by wavelet decomposition in [27]. Restoring images degraded by motion blur is discussed in [26].…”

Section: Introductionmentioning

confidence: 99%

Image Blur Classification and Parameter Identification Using Two-stage Deep Belief Networks

Yan

Shao

2013

Procedings of the British Machine Vision Conference 2013

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Image blur kernel classification and parameter estimation are critical for blind image deblurring. Current dominant approaches use handcrafted blur features that are optimized for a certain type of blur, which is not applicable in real blind deconvolution application when the Point Spread Function (PSF) of the blur is unknown. In this paper, a Two-stage system using Deep Belief Networks (TDBN) is proposed to first classify the blur type and then identify its parameters. To the best of our knowledge, this is the first time that Deep Belief Network (DBN) has been applied to the problem of blur analysis. In the blur type classification, our method attempts to identify the blur type from mixed input of various blurs with different parameters, rather than blur estimation based on the assumption of a single blur type in current methodology. To this aim, a semi-supervised DBN is trained to project the input samples in a discriminative feature space, and then classify those features. Moreover, in the parameter identification, the proposed edge detection on logarithm spectrum helps DBN to identify the blur parameters with very high accuracy. Experiments demonstrate the effectiveness of the proposed methods with better results compared to the state-of-the-art on the Berkeley segmentation dataset and the Pascal VOC 2007 dataset.

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<title>Parametric PSF estimation via sparseness maximization in the wavelet domain</title>

Cited by 10 publications

References 24 publications

Artificial Intelligence

Artificial Intelligence

The Non-parametric Sub-pixel Local Point Spread Function Estimation Is a Well Posed Problem

Image Blur Classification and Parameter Identification Using Two-stage Deep Belief Networks

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