Robust Multi-Image HDR Reconstruction for the Modulo Camera

Photographing scenes with high dynamic range (HDR) poses great challenges to consumer cameras with their limited sensor bit depth. To address this, Zhao et al. recently proposed a novel sensor concept - the modulo camera - which captures the least significant bits of the recorded scene instead of going into saturation. Similar to conventional pipelines, HDR images can be reconstructed from multiple exposures, but significantly fewer images are needed than with a typical saturating sensor. While the concept is appealing, we show that the original reconstruction approach assumes noise-free measurements and quickly breaks down otherwise. To address this, we propose a novel reconstruction algorithm that is robust to image noise and produces significantly fewer artifacts. We theoretically analyze correctness as well as limitations, and show that our approach significantly outperforms the baseline on real data.Comment: to appear at the 39th German Conference on Pattern Recognition (GCPR) 201

Convolutional sparse coding for high dynamic range imaging

Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially-varying pixel exposures. In this paper, we propose a novel algorithm to recover high-quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently-introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher-quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform

High Dynamic Range Imaging with Context-aware Transformer

Avoiding the introduction of ghosts when synthesising LDR images as high dynamic range (HDR) images is a challenging task. Convolutional neural networks (CNNs) are effective for HDR ghost removal in general, but are challenging to deal with the LDR images if there are large movements or oversaturation/undersaturation. Existing dual-branch methods combining CNN and Transformer omit part of the information from non-reference images, while the features extracted by the CNN-based branch are bound to the kernel size with small receptive field, which are detrimental to the deblurring and the recovery of oversaturated/undersaturated regions. In this paper, we propose a novel hierarchical dual Transformer method for ghost-free HDR (HDT-HDR) images generation, which extracts global features and local features simultaneously. First, we use a CNN-based head with spatial attention mechanisms to extract features from all the LDR images. Second, the LDR features are delivered to the Hierarchical Dual Transformer (HDT). In each Dual Transformer (DT), the global features are extracted by the window-based Transformer, while the local details are extracted using the channel attention mechanism with deformable CNNs. Finally, the ghost free HDR image is obtained by dimensional mapping on the HDT output. Abundant experiments demonstrate that our HDT-HDR achieves the state-of-the-art performance among existing HDR ghost removal methods.Comment: 8 pages, 5 figure

Image-based Material Editing

Photo editing software allows digital images to be blurred, warped or re-colored at the touch of a button. However, it is not currently possible to change the material appearance of an object except by painstakingly painting over the appropriate pixels. Here we present a set of methods for automatically replacing one material with another, completely different material, starting with only a single high dynamic range image, and an alpha matte specifying the object. Our approach exploits the fact that human vision is surprisingly tolerant of certain (sometimes enormous) physical inaccuracies. Thus, it may be possible to produce a visually compelling illusion of material transformations, without fully reconstructing the lighting or geometry. We employ a range of algorithms depending on the target material. First, an approximate depth map is derived from the image intensities using bilateral filters. The resulting surface normals are then used to map data onto the surface of the object to specify its material appearance. To create transparent or translucent materials, the mapped data are derived from the object\u27s background. To create textured materials, the mapped data are a texture map. The surface normals can also be used to apply arbitrary bidirectional reflectance distribution functions to the surface, allowing us to simulate a wide range of materials. To facilitate the process of material editing, we generate the HDR image with a novel algorithm, that is robust against noise in individual exposures. This ensures that any noise, which would possibly have affected the shape recovery of the objects adversely, will be removed. We also present an algorithm to automatically generate alpha mattes. This algorithm requires as input two images--one where the object is in focus, and one where the background is in focus--and then automatically produces an approximate matte, indicating which pixels belong to the object. The result is then improved by a second algorithm to generate an accurate alpha matte, which can be given as input to our material editing techniques

Advanced editing methods for image and video sequences

In the context of image and video editing, this thesis proposes methods for modifying the semantic content of a recorded scene. Two different editing problems are approached: First, the removal of ghosting artifacts from high dynamic range (HDR) images recovered from exposure sequences, and second, the removal of objects from video sequences recorded with and without camera motion. These editings need to be performed in a way that the result looks plausible to humans, but without having to recover detailed models about the content of the scene, e.g. its geometry, reflectance, or illumination. The proposed editing methods add new key ingredients, such as camera noise models and global optimization frameworks, that help achieving results that surpass the capabilities of state-of-the-art methods. Using these ingredients, each proposed method defines local visual properties that approximate well the specific editing requirements of each task. These properties are then encoded into a energy function that, when globally minimized, produces the required editing results. The optimization of such energy functions corresponds to Bayesian inference problems that are solved efficiently using graph cuts. The proposed methods are demonstrated to outperform other state-ofthe-art methods. Furthermore, they are demonstrated to work well on complex real-world scenarios that have not been previously addressed in the literature, i.e., highly cluttered scenes for HDR deghosting, and highly dynamic scenes and unconstraint camera motion for object removal from videos.Diese Arbeit schlägt Methoden zur Änderung des semantischen Inhalts einer aufgenommenen Szene im Kontext der Bild-und Videobearbeitung vor. Zwei unterschiedliche Bearbeitungsmethoden werden angesprochen: Erstens, das Entfernen von Ghosting Artifacts (Geist-ähnliche Artefakte) aus High Dynamic Range (HDR) Bildern welche von Belichtungsreihen erstellt wurden und zweitens, das Entfernen von Objekten aus Videosequenzen mit und ohne Kamerabewegung. Das Bearbeiten muss in einer Weise durchgeführt werden, dass das Ergebnis für den Menschen plausibel aussieht, aber ohne das detaillierte Modelle des Szeneninhalts rekonstruiert werden müssen, z.B. die Geometrie, das Reflexionsverhalten, oder Beleuchtungseigenschaften. Die vorgeschlagenen Bearbeitungsmethoden beinhalten neuartige Elemente, etwa Kameralärm-Modelle und globale Optimierungs-Systeme, mit deren Hilfe es möglich ist die Eigenschaften der modernsten existierenden Methoden zu übertreffen. Mit Hilfe dieser Elemente definieren die vorgeschlagenen Methoden lokale visuelle Eigenschaften welche die beschriebenen Bearbeitungsmethoden gut annähern. Diese Eigenschaften werden dann als Energiefunktion codiert, welche, nach globalem minimieren, die gewünschten Bearbeitung liefert. Die Optimierung solcher Energiefunktionen entspricht dem Bayes’schen Inferenz Modell welches effizient mittels Graph-Cut Algorithmen gelöst werden kann. Es wird gezeigt, dass die vorgeschlagenen Methoden den heutigen Stand der Technik übertreffen. Darüber hinaus sind sie nachweislich gut auf komplexe natürliche Szenarien anwendbar, welche in der existierenden Literatur bisher noch nicht angegangen wurden, d.h. sehr unübersichtliche Szenen für HDR Deghosting und sehr dynamische Szenen und unbeschränkte Kamerabewegungen für das Entfernen von Objekten aus Videosequenzen