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Real-world radiance values can range over eight orders of magnitude from starlight to direct sunlight but few digital cameras capture more than three orders in a single Low Dynamic Range (LDR) image. We approach this problem using established High Dynamic Range (HDR) techniques in which multiple images are captured with different exposure times so that all portions of the scene are correctly exposed at least once. These images are then combined to create an HDR image capturing the full range of the scene. HDR capture introduces new challenges; movement in the scene creates faded copies of moving objects, referred to as ghosts. Many techniques have been introduced to handle ghosting, but typically they either address specific types of ghosting, or are computationally very expensive. We address ghosting by first detecting moving objects, then reducing their contribution to the final composite on a frame-by-frame basis. The detection of motion is addressed by performing change detection on exposure-normalized images. Additional special cases are developed based on a priori knowledge of the changing exposures; for example, if exposure is increasing every shot, then any decrease in intensity in the LDR images is a strong indicator of motion. Recent Superpixel over-segmentation techniques are used to refine the detection. We also propose a novel solution for areas that see motion throughout the capture, such as foliage blowing in the wind. Such areas are detected as always moving, and are replaced with information from a single input image, and the replacement of corrupted regions can be tailored to the scenario. We present our approach in the context of a panoramic tele-presence system. Tele-presence systems allow a user to experience a remote environment, aiming to create a realistic sense of "being there" and such a system should therefore provide a high quality visual rendition of the environment. Furthermore, panoramas, by virtue of capturing a greater proportion of a real-world scene, are often exposed to a greater dynamic range than standard photographs. Both facets of this system therefore stand to benefit from HDR imaging techniques. We demonstrate the success of our approach on multiple challenging ghosting scenarios, and compare our results with state-of-the-art methods previously proposed. We also demonstrate computational savings over these methods.

Real-world radiance values can range over eight orders of magnitude from starlight to direct sunlight but few digital cameras capture more than three orders in a single Low Dynamic Range (LDR) image. We approach this problem using established High Dynamic Range (HDR) techniques in which multiple images are captured with different exposure times so that all portions of the scene are correctly exposed at least once. These images are then combined to create an HDR image capturing the full range of the scene. HDR capture introduces new challenges; movement in the scene creates faded copies of moving objects, referred to as ghosts. Many techniques have been introduced to handle ghosting, but typically they either address specific types of ghosting, or are computationally very expensive. We address ghosting by first detecting moving objects, then reducing their contribution to the final composite on a frame-by-frame basis. The detection of motion is addressed by performing change detection on exposure-normalized images. Additional special cases are developed based on a priori knowledge of the changing exposures; for example, if exposure is increasing every shot, then any decrease in intensity in the LDR images is a strong indicator of motion. Recent Superpixel over-segmentation techniques are used to refine the detection. We also propose a novel solution for areas that see motion throughout the capture, such as foliage blowing in the wind. Such areas are detected as always moving, and are replaced with information from a single input image, and the replacement of corrupted regions can be tailored to the scenario. We present our approach in the context of a panoramic tele-presence system. Tele-presence systems allow a user to experience a remote environment, aiming to create a realistic sense of "being there" and such a system should therefore provide a high quality visual rendition of the environment. Furthermore, panoramas, by virtue of capturing a greater proportion of a real-world scene, are often exposed to a greater dynamic range than standard photographs. Both facets of this system therefore stand to benefit from HDR imaging techniques. We demonstrate the success of our approach on multiple challenging ghosting scenarios, and compare our results with state-of-the-art methods previously proposed. We also demonstrate computational savings over these methods.

Real-world radiance values can range over eight orders of magnitude from starlight to direct sunlight but few digital cameras capture more than three orders in a single Low Dynamic Range (LDR) image. We approach this problem using established High Dynamic Range (HDR) techniques in which multiple images are captured with different exposure times so that all portions of the scene are correctly exposed at least once. These images are then combined to create an HDR image capturing the full range of the scene. HDR capture introduces new challenges; movement in the scene creates faded copies of moving objects, referred to as ghosts. Many techniques have been introduced to handle ghosting, but typically they either address specific types of ghosting, or are computationally very expensive. We address ghosting by first detecting moving objects, then reducing their contribution to the final composite on a frame-by-frame basis. The detection of motion is addressed by performing change detection on exposure-normalized images. Additional special cases are developed based on a priori knowledge of the changing exposures; for example, if exposure is increasing every shot, then any decrease in intensity in the LDR images is a strong indicator of motion. Recent Superpixel over-segmentation techniques are used to refine the detection. We also propose a novel solution for areas that see motion throughout the capture, such as foliage blowing in the wind. Such areas are detected as always moving, and are replaced with information from a single input image, and the replacement of corrupted regions can be tailored to the scenario. We present our approach in the context of a panoramic tele-presence system. Tele-presence systems allow a user to experience a remote environment, aiming to create a realistic sense of "being there" and such a system should therefore provide a high quality visual rendition of the environment. Furthermore, panoramas, by virtue of capturing a greater proportion of a real-world scene, are often exposed to a greater dynamic range than standard photographs. Both facets of this system therefore stand to benefit from HDR imaging techniques. We demonstrate the success of our approach on multiple challenging ghosting scenarios, and compare our results with state-of-the-art methods previously proposed. We also demonstrate computational savings over these methods.

Real-world radiance values can range over eight orders of magnitude from starlight to direct sunlight but few digital cameras capture more than three orders in a single Low Dynamic Range (LDR) image. We approach this problem using established High Dynamic Range (HDR) techniques in which multiple images are captured with different exposure times so that all portions of the scene are correctly exposed at least once. These images are then combined to create an HDR image capturing the full range of the scene. HDR capture introduces new challenges; movement in the scene creates faded copies of moving objects, referred to as ghosts. Many techniques have been introduced to handle ghosting, but typically they either address specific types of ghosting, or are computationally very expensive. We address ghosting by first detecting moving objects, then reducing their contribution to the final composite on a frame-by-frame basis. The detection of motion is addressed by performing change detection on exposure-normalized images. Additional special cases are developed based on a priori knowledge of the changing exposures; for example, if exposure is increasing every shot, then any decrease in intensity in the LDR images is a strong indicator of motion. Recent Superpixel over-segmentation techniques are used to refine the detection. We also propose a novel solution for areas that see motion throughout the capture, such as foliage blowing in the wind. Such areas are detected as always moving, and are replaced with information from a single input image, and the replacement of corrupted regions can be tailored to the scenario. We present our approach in the context of a panoramic tele-presence system. Tele-presence systems allow a user to experience a remote environment, aiming to create a realistic sense of "being there" and such a system should therefore provide a high quality visual rendition of the environment. Furthermore, panoramas, by virtue of capturing a greater proportion of a real-world scene, are often exposed to a greater dynamic range than standard photographs. Both facets of this system therefore stand to benefit from HDR imaging techniques. We demonstrate the success of our approach on multiple challenging ghosting scenarios, and compare our results with state-of-the-art methods previously proposed. We also demonstrate computational savings over these methods.

Tato práce navrhuje algoritmus pro pořizování ghost-free HDR videa ze sekvence expozic, který je určený pro implementaci ve vestavěných zařízeních. Vlastnosti algoritmu byly ověřeny implementací ve state-of-the-art architektuře HDR kamery, kde je schopen zpracovávat HDR video s potlačením tzv. ghosting efektu rychlostí až 96 snímků za sekundu na FullHD rozlišení, což více než dostačuje pro zpracování v reálném čase. Navrhovaný ghost-free algoritmus produkuje výstup vizuálně srovnatelný s nejmodernějšími algoritmy, které jsou výpočetně řádově složitější a často je nelze na embedded zařízeních ani implementovat.

Master's thesis is focused on capturing of low dynamic range images using common devices such as camera and its multiple exposure. The main part of thesis is dedicated to composing these images to HDR image, inclusive sequence of images of static scenes, but also dynamic ones. Next part describes tone mapping used for display HDR image on LDR monitors. Moreover, there is given design and implementation of application solving problems mentioned earlier. In the end, the implemented application is evaluated and the possible continuation of this work is stated.