Efficient shift-variant image restoration using deformable filtering (Part I)

Miraut, David; Portilla, Javier

doi:10.1186/1687-6180-2012-100

Cited by 21 publications

(12 citation statements)

References 27 publications

(42 reference statements)

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“…The extension of such approaches to shift-variant filtering requires to approximate the rows of discrete operator H (i.e., the integration kernels) rather than its columns (i.e., the PSFs), which may be challenging since only PSF measurements are straightforward. We refer the interested reader to Miraut and Portilla (2012) 1 this matrix can be considered as a dictionary of all PSFs.…”

Section: Approximations Of Shift-variant Blurmentioning

confidence: 99%

“…The decomposition given by Eq. (28) has been proposed in Flicker and Rigaut (2005) and Miraut and Portilla (2012).…”

Section: Low-rank Approximation On Psf Modesmentioning

confidence: 99%

“…2.3. A different perspective is followed by Flicker and Rigaut (2005) and Miraut and Portilla (2012), see Sec. 2.4, where PSF are decomposed on a few modes computed by principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

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Fast Approximations of Shift-Variant Blur

et al. 2015

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International audienceImage deblurring is essential in high resolution imaging, e.g., astronomy, microscopy or computational photography. Shift-invariant blur is fully characterized by a single point-spread-function (PSF). Blurring is then modeled by a convolution, leading to efficient algorithms for blur simulation and removal that rely on fast Fourier transforms. However, in many different contexts, blur cannot be considered constant throughout the field-of-view, and thus necessitates to model variations of the PSF with the location. These models must achieve a trade-off between the accuracy that can be reached with their flexibility, and their computational efficiency. Several fast approximations of blur have been proposed in the literature. We give a unified presentation of these methods in the light of matrix decompositions of the blurring operator. We establish the connection between different computational tricks that can be found in the litterature and the physical sense of corresponding approximations in terms of equivalent PSFs, physically-based approximations being preferable. We derive an improved approximation that preserves the same desirable low complexity as other fast algorithms while reaching a minimal approximation error. Comparison of theoretical properties and empirical performances of each blur approximation suggests that the proposed general model is preferable for approximation and inversion of a known shift-variant blur

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Section: Approximations Of Shift-variant Blurmentioning

confidence: 99%

“…The decomposition given by Eq. (28) has been proposed in Flicker and Rigaut (2005) and Miraut and Portilla (2012).…”

Section: Low-rank Approximation On Psf Modesmentioning

confidence: 99%

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Fast Approximations of Shift-Variant Blur

et al. 2015

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“…This novel approach results in a significant visual improvement with respect to previous methods. http://asp.eurasipjournals.com/content/2013/1/140 Miraut et al present two papers [20,21] dealing with spatially variant PSF characterization and its corresponding image restoration. In both papers, the authors account for the fact that the PSFs affecting images are not spatially uniform for real imaging devices, but they rather change from one location of the acquired image to another one.…”

mentioning

confidence: 99%

“…In cases specified by the authors, when the PSF does not experience an abrupt change, a smooth PSF field may be used for globally characterizing the blur. In the first paper [20], Miraut and Portilla propose a novel model for representing smooth PSF fields, and for dealing with them efficiently, by means of deformable filtering techniques. A practical, but still model-based and general, methodology is developed on how to perform image restoration on a given image affected by a known, given, smooth PSF field.…”

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confidence: 99%

Advanced statistical tools for enhanced quality digital imaging with realistic capture models

Pižurica

Portilla

Hirakawa

et al. 2013

EURASIP J. Adv. Signal Process.

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EditorialGetting closer to reality in modeling image capture devices is crucial for the improvement of image quality beyond the limits of image restoration algorithms as we know them today. This calls for more accurate statistical modeling of distortions and noise coming from real capture devices (Poisson noise, internal non-linearities, space variant point spread functions due to nonideal optics, chromatic aberrations, etc.). While these effects are often not considered in the restoration algorithms, their impact on the resulting image quality is huge in practice. For example, different nonlinearities (both intrinsic to the imaging device and induced ones, e.g., to make the noise signal independent) can invalidate typically assumed noise models and can also devastate deblurring. Joint modeling of digital and nondigital components (like optics and sensors) or various sources of image distortions (such as color filter array, blur, and noise) will likely yield improvements over the traditional approach to treat them separately. Rapid progress in digital camera technology makes a huge impact on computer vision, surveillance and security systems, production of portable electronic devices, such as smart phones. Physical limits of the sensors are likely to impose trade-offs on picture quality (e.g., in terms of achievable resolution versus noise considerations) that can be dealt with only by smart and device-aware signal processing. Moreover, new challenges arise from new imaging technologies, like in three-dimensional (3D) digital cameras [1,2], and new acquisition modalities, such as compressive sensing [3][4][5][6][7].One of the central topics to this special issue is realistic modeling of noise in digital cameras. With ongoing miniaturization, sensor elements of the camera are becoming increasingly sensitive to noise. In state-of-the-art image sensors, the pixel size is approaching 1 μm. By shrinking the pixel size towards the wavelengths of light that *Correspondence: sanja@telin.ugent.be 1 Department of Telecommunications and Information Processing, Ghent University, Gent 9000, Belgium Full list of author information is available at the end of the article the camera captures, the amount of light received will be further decreased by technical barriers (diffraction effects) [8]. Hence, increasing further the sensor resolution by itself will not necessarily lead to actual gains in image quality. Also, recent improvements in sensor sensitivity allow cameras to operate in very low lighting conditions, but this boosts noise in the acquired images. The negative effects of noise can be largely suppressed by post-processing algorithms, but these require precise knowledge of the noise characteristics to achieve optimal performance. Due to the mismatch between the actual noise characteristics and typically assumed, simplified noise models, the noise level is typically over-or underestimated, affecting adversely the performance of noise reduction.Three papers in this special issue [9][10][11] address the problem of camera ...

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Deblurring for spiral real‐time MRI using convolutional neural networks

Lim

Bliesener

Narayanan

et al. 2020

Magnetic Resonance in Med

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Purpose To develop and evaluate a fast and effective method for deblurring spiral real‐time MRI (RT‐MRI) using convolutional neural networks. Methods We demonstrate a 3‐layer residual convolutional neural networks to correct image domain off‐resonance artifacts in speech production spiral RT‐MRI without the knowledge of field maps. The architecture is motivated by the traditional deblurring approaches. Spatially varying off‐resonance blur is synthetically generated by using discrete object approximation and field maps with data augmentation from a large database of 2D human speech production RT‐MRI. The effect of off‐resonance range, shift‐invariance of blur, and readout durations on deblurring performance are investigated. The proposed method is validated using synthetic and real data with longer readouts, quantitatively using image quality metrics and qualitatively via visual inspection, and with a comparison to conventional deblurring methods. Results Deblurring performance was found superior to a current autocalibrated method for in vivo data and only slightly worse than an ideal reconstruction with perfect knowledge of the field map for synthetic test data. Convolutional neural networks deblurring made it possible to visualize articulator boundaries with readouts up to 8 ms at 1.5 T, which is 3‐fold longer than the current standard practice. The computation time was 12.3 ± 2.2 ms per frame, enabling low‐latency processing for RT‐MRI applications. Conclusion Convolutional neural networks deblurring is a practical, efficient, and field map‐free approach for the deblurring of spiral RT‐MRI. In the context of speech production imaging, this can enable 1.7‐fold improvement in scan efficiency and the use of spiral readouts at higher field strengths such as 3 T.

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Efficient shift-variant image restoration using deformable filtering (Part I)

Cited by 21 publications

References 27 publications

Fast Approximations of Shift-Variant Blur

Fast Approximations of Shift-Variant Blur

Advanced statistical tools for enhanced quality digital imaging with realistic capture models

Deblurring for spiral real‐time MRI using convolutional neural networks

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