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Adaptive dualISO HDR-reconstruction
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7765-1747
2015 (English)In: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281Article in journal (Refereed) Published
Abstract [en]

With the development of modern image sensors enabling flexible image acquisition, single shot HDR imaging is becoming increasingly popular. In this work we capture single shot HDR images using an imaging sensor with spatially varying gain/ISO. In comparison to previous single shot HDR capture based on a single sensor, this allows all incoming photons to be used in the imaging, instead of wasting incoming light using spatially varying ND-filters, commonly used in previous works. 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 [15,10]. 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 previous works, using  adaptive filter kernels [16], our algorithms are based on analysing 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 pattern and noise properties of the sensor.

Place, publisher, year, edition, pages
Springer Publishing Company, 2015.
Keywords [en]
HDR reconstruction; Single shot HDR imaging; DualISO; Statistical image fitlering
National Category
Computer Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-122587DOI: 10.1186/s13640-015-0095-0ISI: 000366324500001OAI: oai:DiVA.org:liu-122587DiVA, id: diva2:868269
Note

Funding agencies: Swedish Foundation for Strategic Research (SSF) [IIS11-0081]; Linkoping University Center for Industrial Information Technology (CENIIT); Swedish Research Council through the Linnaeus Environment CADICS

Available from: 2015-11-10 Created: 2015-11-10 Last updated: 2020-02-18Bibliographically approved
In thesis
1. Physically Based Rendering of Synthetic Objects in Real Environments
Open this publication in new window or tab >>Physically Based Rendering of Synthetic Objects in Real Environments
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This 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.

Abstract [sv]

En 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 i fotot. Genom att på det här sättet möblera ett rum virtuellt, istället för på riktigt, kan man också snabbt testa olika möbleringar och därmed göra ekonomiska besparingar.

Den här avhandlingen bidrar med metoder och algoritmer för att rendera fotorealistiska bilder av virtuella objekt som kan blandas med verkliga fotografier. För att rendera sådana bilder används fysikaliskt baserade simuleringar av hur ljus interagerar med virtuella och verkliga objekt i motivet. För fotorealistiska resultat kräver simuleringarna noggrann modellering av objektens geometri, belysning och materialegenskaper, såsom färg, textur och reflektans.

För att de virtuella objekten ska se verkliga ut är det viktigt att belysa dem med samma ljus som de skulle ha haft om de var en del av den verkliga miljön. Därför är det viktigt att noggrant mäta och modellera ljusförhållanden på de platser i scenen där de virtuella objekten ska placeras. För detta använder vi High Dynamic Range-fotografi, eller HDR. Med hjälp av HDR-fotografi kan vi noggrant mäta hela omfånget av det infallande ljuset i en punkt, från mörka skuggor till direkta ljuskällor. Detta är inte möjligt med traditionella digitalkameror, då det dynamiska omfånget hos vanliga kamerasensorer är begränsat. Avhandlingen beskriver nya metoder för att rekonstruera HDR-bilder som ger mindre brus och artefakter än tidigare metoder. Vi presenterar också metoder för att rendera virtuella objekt som rör sig mellan regioner med olika belysning, eller där belysningen varierar i tiden. Metoder för att representera spatiellt varierande belysning på ett kompakt sätt presenteras också. För att noggrant beskriva hur glansiga ytor sprider eller reflekterar ljus, beskrivs också två nya parametriska modeller som är mer verklighetstrogna än tidigare reflektionsmodeller. I avhandlingen presenteras också en ny metod för effektiv rendering av motiv som är mycket beräkningskrävande, till exempel scener med uppmätta belysningsförhållanden, komplicerade  material, och volumetriska modeller som rök, moln, textiler, biologisk vävnad och vätskor. Metoden bygger på en typ av så kallade Markov Chain Monte Carlo metoder för att simulera ljustransporten i scenen, och är inspirerad av nyligen presenterade resultat inom matematisk statistik.

Metoderna som beskrivs i avhandlingen presenteras i kontexten av fotorealistisk rendering av virtuella objekt i riktiga miljöer, då majoriteten av forskningen utförts inom detta område. Flera av de metoder som presenteras i denna avhandling är dock tillämpbara inom andra domäner, såsom fysiksimulering, datorseende och vetenskaplig visualisering.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. p. 135
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1717
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-122588 (URN)10.3384/diss.diva-122588 (DOI)978-91-7685-912-4 (ISBN)
Public defence
2015-12-04, Domteatern, Visualiseringscenter C, Kungsgatan 54, Norrköping, 09:15 (English)
Opponent
Supervisors
Available from: 2015-11-10 Created: 2015-11-10 Last updated: 2019-11-15Bibliographically approved
2. Computational Photography: High Dynamic Range and Light Fields
Open this publication in new window or tab >>Computational Photography: High Dynamic Range and Light Fields
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The introduction and recent advancements of computational photography have revolutionized the imaging industry. Computational photography is a combination of imaging techniques at the intersection of various fields such as optics, computer vision, and computer graphics. These methods enhance the capabilities of traditional digital photography by applying computational techniques both during and after the capturing process. This thesis targets two major subjects in this field: High Dynamic Range (HDR) image reconstruction and Light Field (LF) compressive capturing, compression, and real-time rendering.

The first part of the thesis focuses on the HDR images that concurrently contain detailed information from the very dark shadows to the brightest areas in the scenes. One of the main contributions presented in this thesis is the development of a unified reconstruction algorithm for spatially variant exposures in a single image. This method is based on a camera noise model, and it simultaneously resamples, reconstructs, denoises, and demosaics the image while extending its dynamic range. Furthermore, the HDR reconstruction algorithm is extended to adapt to the local features of the image, as well as the noise statistics, to preserve the high-frequency edges during reconstruction.

In the second part of this thesis, the research focus shifts to the acquisition, encoding, reconstruction, and rendering of light field images and videos in a real-time setting. Unlike traditional integral photography, a light field captures the information of the dynamic environment from all angles, all points in space, and all spectral wavelength and time. This thesis employs sparse representation to provide an end-to-end solution to the problem of encoding, real-time reconstruction, and rendering of high dimensional light field video data sets. These solutions are applied on various types of data sets, such as light fields captured with multi-camera systems or hand-held cameras equipped with micro-lens arrays, and spherical light fields. Finally, sparse representation of light fields was utilized for developing a single sensor light field video camera equipped with a color-coded mask. A new compressive sensing model is presented that is suitable for dynamic scenes with temporal coherency and is capable of reconstructing high-resolution light field videos.  

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. p. 122
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2046
National Category
Media Engineering
Identifiers
urn:nbn:se:liu:diva-163693 (URN)10.3384/diss.diva-163693 (DOI)9789179299057 (ISBN)
Public defence
2020-02-28, Domteatern, Visualiseringscenter C, Kungsgatan 54, 60233, Norrköping, 09:15 (English)
Opponent
Supervisors
Available from: 2020-02-18 Created: 2020-02-18 Last updated: 2020-03-18Bibliographically approved

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Hajisharif, SaghiKronander, JoelUnger, Jonas

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