560 research outputs found
FaceShop: Deep Sketch-based Face Image Editing
We present a novel system for sketch-based face image editing, enabling users
to edit images intuitively by sketching a few strokes on a region of interest.
Our interface features tools to express a desired image manipulation by
providing both geometry and color constraints as user-drawn strokes. As an
alternative to the direct user input, our proposed system naturally supports a
copy-paste mode, which allows users to edit a given image region by using parts
of another exemplar image without the need of hand-drawn sketching at all. The
proposed interface runs in real-time and facilitates an interactive and
iterative workflow to quickly express the intended edits. Our system is based
on a novel sketch domain and a convolutional neural network trained end-to-end
to automatically learn to render image regions corresponding to the input
strokes. To achieve high quality and semantically consistent results we train
our neural network on two simultaneous tasks, namely image completion and image
translation. To the best of our knowledge, we are the first to combine these
two tasks in a unified framework for interactive image editing. Our results
show that the proposed sketch domain, network architecture, and training
procedure generalize well to real user input and enable high quality synthesis
results without additional post-processing.Comment: 13 pages, 20 figure
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging
Recently, impressive denoising results have been achieved by Bayesian
approaches which assume Gaussian models for the image patches. This improvement
in performance can be attributed to the use of per-patch models. Unfortunately
such an approach is particularly unstable for most inverse problems beyond
denoising. In this work, we propose the use of a hyperprior to model image
patches, in order to stabilize the estimation procedure. There are two main
advantages to the proposed restoration scheme: Firstly it is adapted to
diagonal degradation matrices, and in particular to missing data problems (e.g.
inpainting of missing pixels or zooming). Secondly it can deal with signal
dependent noise models, particularly suited to digital cameras. As such, the
scheme is especially adapted to computational photography. In order to
illustrate this point, we provide an application to high dynamic range imaging
from a single image taken with a modified sensor, which shows the effectiveness
of the proposed scheme.Comment: Some figures are reduced to comply with arxiv's size constraints.
Full size images are available as HAL technical report hal-01107519v5, IEEE
Transactions on Computational Imaging, 201
Non-linear Aggregation of Filters to Improve Image Denoising.
We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear
fashion, using a new metric of pixel proximity based on how the pool of
filters reaches a consensus. We provide a theoretical bound to support
our aggregation scheme, its numerical performance is illustrated and we
show that the aggregate significantly outperforms each of the preliminary
filters
Deep spatial and tonal data optimisation for homogeneous diffusion inpainting
Difusion-based inpainting can reconstruct missing image areas with high quality from sparse data, provided that their location and their values are well optimised. This is particularly useful for applications such as image compression, where the
original image is known. Selecting the known data constitutes a challenging optimisation problem, that has so far been only
investigated with model-based approaches. So far, these methods require a choice between either high quality or high speed
since qualitatively convincing algorithms rely on many time-consuming inpaintings. We propose the frst neural network
architecture that allows fast optimisation of pixel positions and pixel values for homogeneous difusion inpainting. During
training, we combine two optimisation networks with a neural network-based surrogate solver for difusion inpainting. This
novel concept allows us to perform backpropagation based on inpainting results that approximate the solution of the inpainting equation. Without the need for a single inpainting during test time, our deep optimisation accelerates data selection by
more than four orders of magnitude compared to common model-based approaches. This provides real-time performance
with high quality results
- …