Browsing by Author "Heidrich, Wolfgang"

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    MetaISP -- Exploiting Global Scene Structure for Accurate Multi-Device Color Rendition
    (The Eurographics Association, 2023) Souza, Matheus; Heidrich, Wolfgang; Guthe, Michael; Grosch, Thorsten
    Image signal processors (ISPs) are historically grown legacy software systems for reconstructing color images from noisy raw sensor measurements. Each smartphone manufacturer has developed its ISPs with its own characteristic heuristics for improving the color rendition, for example, skin tones and other visually essential colors. The recent interest in replacing the historically grown ISP systems with deep-learned pipelines to match DSLR's image quality improves structural features in the image. However, these works ignore the superior color processing based on semantic scene analysis that distinguishes mobile phone ISPs from DSLRs. Here we present MetaISP, a single model designed to learn how to translate between the color and local contrast characteristics of different devices. MetaISP takes the RAW image from device A as input and translates it to RGB images that inherit the appearance characteristics of devices A, B, and C. We achieve this result by employing a lightweight deep learning technique that conditions its output appearance based on the device of interest. In this approach, we leverage novel attention mechanisms inspired by cross-covariance learn global scene semantics. Additionally, we make use of metadata that typically accompanies raw images, and we estimate scene illuminants when they are not available.
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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.