High dynamic range video by spatially non-regular optical filtering

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

doi:10.1109/icip.2012.6467470

Cited by 21 publications

(30 citation statements)

References 16 publications

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“…The exposure time is set to τ = 1/200 seconds and the camera parameters are those of a Canon 7D camera set to ISO 200 (g = 0.87, σ 2 R = 30, µ R = 2048, v sat = 15000) [27]. Figure 7 shows extracts of the results obtained by the proposed method, by PLEV [28] (basically an adaptation of PLE to the same single image framework) and by Schöberl et al [36] for the random pattern and by Nayar et Mitsunaga [18] using the regular pattern. The percentage of unknown pixels in the considered extracts is 50% (it is nearly the same for both the regular and non-regular pattern).…”

Section: Methodsmentioning

confidence: 99%

“…This observation led us to choose the non-regular pattern in the proposed approach. [36], Nayar and Mitsunaga [18]. 50% missing pixels (for both random and regular pattern).…”

Section: A Spatially Varying Exposure Acquisition Modelmentioning

confidence: 99%

“…Motivated by the aliasing problems of regular sampling patterns, Schöberl et al [36] propose to use spatially varying exposures on a nonregular pattern. Figure 7 shows examples of both acquisition patterns.…”

Section: A Spatially Varying Exposure Acquisition Modelmentioning

confidence: 99%

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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

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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.

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Section: Methodsmentioning

confidence: 99%

“…This observation led us to choose the non-regular pattern in the proposed approach. [36], Nayar and Mitsunaga [18]. 50% missing pixels (for both random and regular pattern).…”

Section: A Spatially Varying Exposure Acquisition Modelmentioning

confidence: 99%

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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

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show abstract

“…If the sampling patterns are regular, aliasing artefacts may appear in the interpolation. On the contrary, if a random or pseudo-random pattern is used aliasing can be avoided or significantly suppressed [6,187]. To capture HDR color images the spatially varying exposure patterns can be combined with Bayer filter designs, for example by using a spatially varying exposure and color filter array [161,228] instead of a traditional bayer filter.…”

Section: Spatially Varying Sensor Exposurementioning

confidence: 99%

Physically Based Rendering of Synthetic Objects in Real Environments

Kronander¹

2015

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Description of the cover imageRendering of virtual furniture illuminated with spatially varying real world illumination captured in a physical environment at IKEA Communications AB.The virtual scene was modeled by Sören and Per Larsson. Physically based rendering of synthetic objects in real environmentsCopyright © 2015 Joel Kronander (unless otherwise noted) Division of Media and Information TechnologyDepartment of Science and Technology Campus Norrköping, Printed in Sweden by LiU-Tryck, Linköping, 2015 AbstractThis thesis presents methods for photorealistic rendering of virtual objects so that they can be seamlessly composited into images of the real world. To generate predictable and consistent results, we study physically based methods, which simulate how light propagates in a mathematical model of the augmented scene. This computationally challenging problem demands both efficient and accurate simulation of the light transport in the scene, as well as detailed modeling of the geometries, illumination conditions, and material properties. In this thesis, we discuss and formulate the challenges inherent in these steps and present several methods to make the process more efficient.In particular, the material contained in this thesis addresses four closely related areas: HDR imaging, IBL, reflectance modeling, and efficient rendering. The thesis presents a new, statistically motivated algorithm for HDR reconstruction from raw camera data combining demosaicing, denoising, and HDR fusion in a single processing operation. The thesis also presents practical and robust methods for rendering with spatially and temporally varying illumination conditions captured using omnidirectional HDR video. Furthermore, two new parametric BRDF models are proposed for surfaces exhibiting wide angle gloss. Finally, the thesis also presents a physically based light transport algorithm based on Markov Chain Monte Carlo methods that allows approximations to be used in place of exact quantities, while still converging to the exact result. As illustrated in the thesis, the proposed algorithm enables efficient rendering of scenes with glossy transfer and heterogenous participating media.v Populärvetenskaplig SammanfattningEn av de största utmaningarna inom datorgrafik är att syntetisera, eller rendera, fotorealistiska bilder. Fotorealistisk rendering används idag inom många tillämpningsområden såsom specialeffekter i film, datorspel, produktvisualisering och virtuell verklighet. I många praktiska tillämpningar av fotorealistisk rendering är det viktigt att kunna placera in virtuella objekt i fotografier, så att de virtuella objekten ser verkliga ut. IKEA-katalogen, till exempel, produceras i många olika versioner för att passa olika länder och regioner. Grunden till de flesta bilderna i katalogen är oftast densamma, men symboler och standardmått på möbler varierar ofta för olika versioner av katalogen. Istället för att fotografera varje version separat kan man använda ett grundfotografi och lägga in olika virtuella objekt såsom möbler...

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“…Here, a single sensor image is used where the response to incident light varies over the sensor. Most previous works achieved this by placing a spatially varying array of ND filters in front of the sensor [2,24,25,27]. Its most familiar application is color imaging via a color filter array (e.g., the Bayer pattern [6]).…”

Section: Hdr Capturementioning

confidence: 99%

Adaptive dualISO HDR reconstruction

Hajisharif

Kronander

Unger

2015

J Image Video Proc.

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With the development of modern image sensors enabling flexible image acquisition, single shot high dynamic range (HDR) imaging is becoming increasingly popular. In this work, we capture single shot HDR images using an imaging sensor with spatially varying gain/ISO. This allows all incoming photons to be used in the imaging. Previous methods on single shot HDR capture use spatially varying neutral density (ND) filters which lead to wasting incoming light. The main technical contribution in this work is an extension of previous HDR reconstruction approaches for single shot HDR imaging based on local polynomial approximations (Kronander et al., Unified HDR reconstruction from raw CFA data, 2013; Hajisharif et al., HDR reconstruction for alternating gain (ISO) sensor readout, 2014). Using a sensor noise model, these works deploy a statistically informed filtering operation to reconstruct HDR pixel values. However, instead of using a fixed filter size, we introduce two novel algorithms for adaptive filter kernel selection. Unlike a previous work, using adaptive filter kernels (Signal Process Image Commun 29(2):203-215, 2014), our algorithms are based on analyzing the model fit and the expected statistical deviation of the estimate based on the sensor noise model. Using an iterative procedure, we can then adapt the filter kernel according to the image structure and the statistical image noise. Experimental results show that the proposed filter de-noises the noisy image carefully while well preserving the important image features such as edges and corners, outperforming previous methods. To demonstrate the robustness of our approach, we have exploited input images from raw sensor data using a commercial off-the-shelf camera. To further analyze our algorithm, we have also implemented a camera simulator to evaluate different gain patterns and noise properties of the sensor.

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High dynamic range video by spatially non-regular optical filtering

Cited by 21 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

Physically Based Rendering of Synthetic Objects in Real Environments

Adaptive dualISO HDR reconstruction

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