VMV19

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https://diglib.eg.org/handle/10.2312/2632811

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Browsing VMV19 by Subject "Computational photography"

Now showing 1 - 3 of 3
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    Consistent Filtering of Videos and Dense Light-Fields Without Optic-Flow
    (The Eurographics Association, 2019) Shekhar, Sumit; Semmo, Amir; Trapp, Matthias; Tursun, Okan; Pasewaldt, Sebastian; Myszkowski, Karol; Döllner, Jürgen; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
    A convenient post-production video processing approach is to apply image filters on a per-frame basis. This allows the flexibility of extending image filters-originally designed for still images-to videos. However, per-image filtering may lead to temporal inconsistencies perceived as unpleasant flickering artifacts, which is also the case for dense light-fields due to angular inconsistencies. In this work, we present a method for consistent filtering of videos and dense light-fields that addresses these problems. Our assumption is that inconsistencies-due to per-image filtering-are represented as noise across the image sequence. We thus perform denoising across the filtered image sequence and combine per-image filtered results with their denoised versions. At this, we use saliency based optimization weights to produce a consistent output while preserving the details simultaneously. To control the degree-of-consistency in the final output, we implemented our approach in an interactive real-time processing framework. Unlike state-of-the-art inconsistency removal techniques, our approach does not rely on optic-flow for enforcing coherence. Comparisons and a qualitative evaluation indicate that our method provides better results over state-of-the-art approaches for certain types of filters and applications.
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    Polarization Demosaicking for Monochrome and Color Polarization Focal Plane Arrays
    (The Eurographics Association, 2019) Qiu, Simeng; Fu, Qiang; Wang, Congli; Heidrich, Wolfgang ; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
    Division-of-focal-plane (DoFP) polarization image sensors allow for snapshot imaging of linear polarization effects with inexpensive and straightforward setups. However, conventional interpolation based image reconstruction methods for such sensors produce unreliable and noisy estimates of quantities such as degree of linear polarization (DoLP) or angle of linear polarization (AoLP). In this paper, we propose a polarization demosaicking algorithm by inverting the polarization image formation model for both monochrome and color DoFP cameras. Compared to previous interpolation methods, our approach can significantly reduce noise induced artifacts and drastically increase the accuracy in estimating polarization states. We evaluate and demonstrate the performance of the methods on a new high-resolution color polarization dataset. Simulation and experimental results show that the proposed reconstruction and analysis tools offer an effective solution to polarization imaging.
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    Reconfigurable Snapshot HDR Imaging Using Coded Masks and Inception Network
    (The Eurographics Association, 2019) Alghamdi, Masheal; Fu, Qiang; Thabet, Ali; Heidrich, Wolfgang ; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
    High Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this paper we propose a joint design for snapshot HDR imaging by devising a spatially-varying modulation mask in the hardware as well as building an inception network to reconstruct the HDR image. We achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware-software solution offers a flexible yet robust way to modulating per-pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparison results show that our method outperforms state of the art in terms of visual perception quality.