141 research outputs found
High Resolution 3D Shape Texture from Multiple Videos
International audienceWe examine the problem of retrieving high resolution textures of objects observed in multiple videos under small object deformations. In the monocular case, the data redundancy necessary to reconstruct a high-resolution image stems from temporal accumulation. This has been vastly explored and is known as super-resolution. On the other hand, a handful of methods have considered the texture of a static 3D object observed from several cameras, where the data redundancy is obtained through the different viewpoints. We introduce a unified framework to leverage both possibilities for the estimation of a high resolution texture of an object. This framework uniformly deals with any related geometric variability introduced by the acquisition chain or by the evolution over time. To this goal we use 2D warps for all viewpoints and all temporal frames and a linear projection model from texture to image space. Despite its simplicity, the method is able to successfully handle different views over space and time. As shown experimentally, it demonstrates the interest of temporal information that improves the texture quality. Additionally, we also show that our method outperforms state of the art multi-view super-resolution methods that exist for the static case
A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer
Light-sheet microscopy: a tutorial
This paper is intended to give a comprehensive review of light-sheet (LS) microscopy from an optics perspective. As such, emphasis is placed on the advantages that LS microscope configurations present, given the degree of freedom gained by uncoupling the excitation and detection arms. The new imaging properties are first highlighted in terms of optical parameters and how these have enabled several biomedical applications. Then, the basics are presented for understanding how a LS microscope works. This is followed by a presentation of a tutorial for LS microscope designs, each working at different resolutions and for different applications. Then, based on a numerical Fourier analysis and given the multiple possibilities for generating the LS in the microscope (using Gaussian, Bessel, and Airy beams in the linear and nonlinear regimes), a systematic comparison of their optical performance is presented. Finally, based on advances in optics and photonics, the novel optical implementations possible in a LS microscope are highlighted.Peer ReviewedPostprint (published version
FaceLit: Neural 3D Relightable Faces
We propose a generative framework, FaceLit, capable of generating a 3D face
that can be rendered at various user-defined lighting conditions and views,
learned purely from 2D images in-the-wild without any manual annotation. Unlike
existing works that require careful capture setup or human labor, we rely on
off-the-shelf pose and illumination estimators. With these estimates, we
incorporate the Phong reflectance model in the neural volume rendering
framework. Our model learns to generate shape and material properties of a face
such that, when rendered according to the natural statistics of pose and
illumination, produces photorealistic face images with multiview 3D and
illumination consistency. Our method enables photorealistic generation of faces
with explicit illumination and view controls on multiple datasets - FFHQ,
MetFaces and CelebA-HQ. We show state-of-the-art photorealism among 3D aware
GANs on FFHQ dataset achieving an FID score of 3.5.Comment: CVPR 202
Learned Multi-View Texture Super-Resolution
We present a super-resolution method capable of creating a high-resolution
texture map for a virtual 3D object from a set of lower-resolution images of
that object. Our architecture unifies the concepts of (i) multi-view
super-resolution based on the redundancy of overlapping views and (ii)
single-view super-resolution based on a learned prior of high-resolution (HR)
image structure. The principle of multi-view super-resolution is to invert the
image formation process and recover the latent HR texture from multiple
lower-resolution projections. We map that inverse problem into a block of
suitably designed neural network layers, and combine it with a standard
encoder-decoder network for learned single-image super-resolution. Wiring the
image formation model into the network avoids having to learn perspective
mapping from textures to images, and elegantly handles a varying number of
input views. Experiments demonstrate that the combination of multi-view
observations and learned prior yields improved texture maps.Comment: 11 pages, 5 figures, 2019 International Conference on 3D Vision (3DV
Super Resolution of Wavelet-Encoded Images and Videos
In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, and wavelet-encoding for image/video compression. Due to drawbacks of widely used discrete cosine transform in image and video compression, a considerable amount of literature is devoted to wavelet-based methods. However, since wavelets are shift-variant, existing methods cannot utilize wavelet subbands efficiently. In order to overcome this drawback, we establish and explore the direct relationship between the subbands under a translational shift, for image registration and super resolution. We then employ our devised in-band methodology, in a motion compensated video compression framework, to demonstrate the effective usage of wavelet subbands. Super resolution can also be used as a post-processing step in video compression in order to decrease the size of the video files to be compressed, with downsampling added as a pre-processing step. Therefore, we present a video compression scheme that utilizes super resolution to reconstruct the high frequency information lost during downsampling. In addition, super resolution is a crucial post-processing step for satellite imagery, due to the fact that it is hard to update imaging devices after a satellite is launched. Thus, we also demonstrate the usage of our devised methods in enhancing resolution of pansharpened multispectral images
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