Building a high dynamic range video sensor with spatially nonregular optical filtering

Schöberl, Michael; Belz, Alexander; Nowak, Arne; Seiler, Jurgen; Kaup, André; Foessel, Siegfried

doi:10.1117/12.928858

Cited by 9 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) Real data: The feasibility of the SVE random pattern has been shown in [34] and that of the SVE regular pattern in [33]. Nevertheless, these acquisition systems are still not available for general usage.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, With an Application to HDR Imaging

Aguerrebere

Almansa

Delon

et al. 2017

IEEE Trans. Comput. Imaging

View full text Add to dashboard Cite

Abstract-Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.Index Terms-Non-local patch-based restoration, Bayesian restoration, Maximum a Posteriori, Gaussian Mixture Models, hyper-prior, conjugate distributions, high dynamic range imaging, single shot HDR, hierarchical models.

show abstract

Section: Methodsmentioning

confidence: 99%

“…An optical mask with spatially varying transmittance is placed adjacent to a conventional image sensor, thus controlling the amount of light that reaches each pixel (see Figure 7) [18], [33], [34].…”

Section: Snapshotmentioning

confidence: 99%

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, With an Application to HDR Imaging

Aguerrebere

Almansa

Delon

et al. 2017

IEEE Trans. Comput. Imaging

View full text Add to dashboard Cite

show abstract

“…The feasibility of the SVE random pattern has been shown in [14] and that of the SVE regular pattern in [18]. Nevertheless, these acquisition systems are still not available for general usage.…”

Section: Real Datamentioning

confidence: 99%

“…As presented in [12,18,14], an optical mask with spatially varying transmittance can be placed adjacent to a conventional image sensor to give different exposure levels to the pixels. This optical mask does not change the acquisition process of the sensor, whether using a conventional CCD or CMOS sensor.…”

Section: Spatially Varying Exposure Acquisition Modelmentioning

confidence: 99%

“…The reconstruction of the irradiance image is then performed using a frequency selective extrapolation algorithm [16] which iteratively generates a sparse model for each image patch as a weighted superposition of the two-dimensional Fourier basis functions. In [14], Schöberl et al present a practical methodology for the construction of a spatially varying exposures mask with a non-regular pattern.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Single shot high dynamic range imaging using piecewise linear estimators

Aguerrebere

Almansa

Gousseau

et al. 2014

2014 IEEE International Conference on Computational Photography (ICCP)

View full text Add to dashboard Cite

Building high dynamic range (HDR) images by combining photographs captured with different exposure times present several drawbacks, such as the need for global alignment and motion estimation in order to avoid ghosting artifacts. The concept of spatially varying pixel exposures (SVE) proposed by Nayar et al. enables to capture in only one shot a very large range of exposures while avoiding these limitations. In this paper, we propose a novel approach to generate HDR images from a single shot acquired with spatially varying pixel exposures. The proposed method makes use of the assumption stating that the distribution of patches in an image is well represented by a Gaussian Mixture Model. Drawing on a precise modeling of the camera acquisition noise, we extend the piecewise linear estimation strategy developed by Yu et al. for image restoration. The proposed method permits to reconstruct an irradiance image by simultaneously estimating saturated and under-exposed pixels and denoising existing ones, showing significant improvements over existing approaches.

show abstract

Learning HDR video reconstruction for dual-exposure sensors with temporally-alternating exposures

Cogalan

Bemana

Seidel

et al. 2022

Computers & Graphics

View full text Add to dashboard Cite

Building a high dynamic range video sensor with spatially nonregular optical filtering

Cited by 9 publications

References 16 publications

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, With an Application to HDR Imaging

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, With an Application to HDR Imaging

Single shot high dynamic range imaging using piecewise linear estimators

Learning HDR video reconstruction for dual-exposure sensors with temporally-alternating exposures

Contact Info

Product

Resources

About