Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Producing a high dynamic range (HDR) image from a set of images with different exposures is a challenging process for dynamic scenes. A category of existing techniques first register the input images to a reference image and then merge the aligned images into an HDR image. However, the artifacts of the registration usually appear as ghosting and tearing in the final HDR images. In this paper, we propose a learning-based approach to address this problem for dynamic scenes. We use a convolutional neural network (CNN) as our learning model and present and compare three different system architectures to model the HDR merge process. Furthermore, we create a large dataset of input LDR images and their corresponding ground truth HDR images to train our system. We demonstrate the performance of our system by producing high-quality HDR images from a set of three LDR images. Experimental results show that our method consistently produces better results than several state-of-the-art approaches on challenging scenes.

Deep high dynamic range imaging of dynamic scenes

We propose an automatic video inpainting algorithm which relies on the optimization of a global, patch-based functional. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects, and moving background. Furthermore, we achieve this in an order of magnitude less execution time with respect to the state-of-the-art. We are also able to achieve good quality results on high-definition videos. Finally, we provide specific algorithmic details to make implementation of our algorithm as easy as possible. The resulting algorithm requires no segmentation or manual input other than the definition of the inpainting mask and can deal with a wider variety of situations than is handled by previous work.

https://hal.archives-ouvertes.fr/hal-00937795/document

Video Inpainting of Complex Scenes

Although High Dynamic Range (HDR) imaging has been the subject of significant research over the past fifteen years, the goal of acquiring cinema-quality HDR images of fast-moving scenes using available components has not yet been achieved. In this work, we present an optical architecture for HDR imaging that allows simultaneous capture of high, medium, and low-exposure images on three sensors at high fidelity with efficient use of the available light. We also present an HDR merging algorithm to complement this architecture, which avoids undesired artifacts when there is a large exposure difference between the images. We implemented a prototype high-definition HDR-video system and we present still frames from the acquired HDR video, tonemapped with various techniques.

A versatile HDR video production system

A single captured image of a real-world scene is usually insufficient to reveal all the details due to under- or over-exposed regions To solve this problem, images of the same scene can be first captured under different exposure settings and then combined into a single image using image fusion techniques In this paper, we propose a novel probabilistic model-based fusion technique for multi-exposure images Unlike previous multi-exposure fusion methods, our method aims to achieve an optimal balance between two quality measures, ie, local contrast and color consistency, while combining the scene details revealed under different exposures A generalized random walks framework is proposed to calculate a globally optimal solution subject to the two quality measures by formulating the fusion problem as probability estimation Experiments demonstrate that our algorithm generates high-quality images at low computational cost Comparisons with a number of other techniques show that our method generates better results in most cases

Generalized Random Walks for Fusion of Multi-Exposure Images

Given a multi-exposure sequence of a scene, our aim is to recover the absolute irradiance falling onto a linear camera sensor. The established approach is to perform a weighted average of the scaled input exposures. However, there is no clear consensus on the appropriate weighting to use. We propose a weighting function that produces statistically optimal estimates under the assumption of compound-Gaussian noise. Our weighting is based on a calibrated camera model that accounts for all noise sources. This model also allows us to simultaneously estimate the irradiance and its uncertainty. We evaluate our method on simulated and real world photographs, and show that we consistently improve the signal-to-noise ratio over previous approaches. Finally, we show the effectiveness of our model for optimal exposure sequence selection and HDR image denoising.

/pdf/optimal-hdr-reconstruction-with-linear-digital-cameras-40nnns0s4n.pdf

Optimal HDR reconstruction with linear digital cameras

We propose a method for removing marked dynamic objects from videos captured with a free-moving camera, so long as the objects occlude parts of the scene with a static background. Our approach takes as input a video, a mask marking the object to be removed, and a mask marking the dynamic objects to remain in the scene. To inpaint a frame, we align other candidate frames in which parts of the missing region are visible. Among these candidates, a single source is chosen to fill each pixel so that the final arrangement is color-consistent. Intensity differences between sources are smoothed using gradient domain fusion. Our frame alignment process assumes that the scene can be approximated using piecewise planar geometry: A set of homographies is estimated for each frame pair, and one each is selected for aligning pixels such that the color-discrepancy is minimized and the epipolar constraints are maintained. We provide experimental validation with several real-world video sequences to demonstrate that, unlike in previous work, inpainting videos shot with free-moving cameras does not necessarily require estimation of absolute camera positions and per-frame per-pixel depth maps.

/pdf/background-inpainting-for-videos-with-dynamic-objects-and-a-2fu9yf3trb.pdf

Background inpainting for videos with dynamic objects and a free-moving camera

Removing dynamic objects from videos is an extremely challenging problem that even visual effects professionals often solve with time-consuming manual frame-by-frame editing We propose a new approach to video completion that can deal with complex scenes containing dynamic background and non-periodical moving objects We build upon the idea that the spatio-temporal hole left by a removed object can be filled with data available on other regions of the video where the occluded objects were visible Video completion is performed by solving a large combinatorial problem that searches for an optimal pattern of pixel offsets from occluded to unoccluded regions Our contribution includes an energy functional that generalizes well over different scenes with stable parameters, and that has the desirable convergence properties for a graph-cut-based optimization We provide an interface to guide the completion process that both reduces computation time and allows for efficient correction of small errors in the result We demonstrate that our approach can effectively complete complex, high-resolution occlusions that are greater in difficulty than what existing methods have shown © 2012 Wiley Periodicals, Inc

/pdf/how-not-to-be-seen-object-removal-from-videos-of-crowded-8wfsr0iz91.pdf

How Not to Be Seen — Object Removal from Videos of Crowded Scenes

High dynamic range reconstruction of dynamic scenes requires careful handling of dynamic objects to prevent ghosting. However, in a recent review, Srikantha et al. [2012] conclude that "there is no single best method and the selection of an approach depends on the user's goal". We attempt to solve this problem with a novel approach that models the noise distribution of color values. We estimate the likelihood that a pair of colors in different images are observations of the same irradiance, and we use a Markov random field prior to reconstruct irradiance from pixels that are likely to correspond to the same static scene object. Dynamic content is handled by selecting a single low dynamic range source image and hand-held capture is supported through homography-based image alignment. Our noise-based reconstruction method achieves better ghost detection and removal than state-of-the-art methods for cluttered scenes with large object displacements. As such, our method is broadly applicable and helps move the field towards a single method for dynamic scene HDR reconstruction.

/pdf/automatic-noise-modeling-for-ghost-free-hdr-reconstruction-1v58wb0aud.pdf

Automatic noise modeling for ghost-free HDR reconstruction

We describe a data structure for three-dimensional Nef complexes, algorithms for boolean operations on them, and our implementation of data structure and algorithms. Nef polyhedra were introduced by W. Nef in his seminal 1978 book on polyhedra. They are the closure of half-spaces under boolean operations and can represent non-manifold situations, open and closed boundaries, and mixed dimensional complexes. Our focus lies on the generality of the data structure, the completeness of the algorithms, and the exactness and efficiency of the implementation. In particular, all degeneracies are handled.

Miguel Granados

Papers

Optimal HDR reconstruction with linear digital cameras

Background inpainting for videos with dynamic objects and a free-moving camera

How Not to Be Seen — Object Removal from Videos of Crowded Scenes

Automatic noise modeling for ghost-free HDR reconstruction

Boolean operations on 3D selective Nef complexes: Data structure, algorithms, and implementation