318 research outputs found
Learned Perceptual Image Enhancement
Learning a typical image enhancement pipeline involves minimization of a loss
function between enhanced and reference images. While L1 and L2 losses are
perhaps the most widely used functions for this purpose, they do not
necessarily lead to perceptually compelling results. In this paper, we show
that adding a learned no-reference image quality metric to the loss can
significantly improve enhancement operators. This metric is implemented using a
CNN (convolutional neural network) trained on a large-scale dataset labelled
with aesthetic preferences of human raters. This loss allows us to conveniently
perform back-propagation in our learning framework to simultaneously optimize
for similarity to a given ground truth reference and perceptual quality. This
perceptual loss is only used to train parameters of image processing operators,
and does not impose any extra complexity at inference time. Our experiments
demonstrate that this loss can be effective for tuning a variety of operators
such as local tone mapping and dehazing
Convergence of algorithms for reconstructing convex bodies and directional measures
We investigate algorithms for reconstructing a convex body in from noisy measurements of its support function or its brightness
function in directions . The key idea of these algorithms is
to construct a convex polytope whose support function (or brightness
function) best approximates the given measurements in the directions
(in the least squares sense). The measurement errors are assumed
to be stochastically independent and Gaussian. It is shown that this procedure
is (strongly) consistent, meaning that, almost surely, tends to in
the Hausdorff metric as . Here some mild assumptions on the
sequence of directions are needed. Using results from the theory of
empirical processes, estimates of rates of convergence are derived, which are
first obtained in the metric and then transferred to the Hausdorff
metric. Along the way, a new estimate is obtained for the metric entropy of the
class of origin-symmetric zonoids contained in the unit ball. Similar results
are obtained for the convergence of an algorithm that reconstructs an
approximating measure to the directional measure of a stationary fiber process
from noisy measurements of its rose of intersections in directions
. Here the Dudley and Prohorov metrics are used. The methods are
linked to those employed for the support and brightness function algorithms via
the fact that the rose of intersections is the support function of a projection
body.Comment: Published at http://dx.doi.org/10.1214/009053606000000335 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
Inversion by Direct Iteration (InDI) is a new formulation for supervised
image restoration that avoids the so-called ``regression to the mean'' effect
and produces more realistic and detailed images than existing regression-based
methods. It does this by gradually improving image quality in small steps,
similar to generative denoising diffusion models. Image restoration is an
ill-posed problem where multiple high-quality images are plausible
reconstructions of a given low-quality input. Therefore, the outcome of a
single step regression model is typically an aggregate of all possible
explanations, therefore lacking details and realism. The main advantage of InDI
is that it does not try to predict the clean target image in a single step but
instead gradually improves the image in small steps, resulting in better
perceptual quality. While generative denoising diffusion models also work in
small steps, our formulation is distinct in that it does not require knowledge
of any analytic form of the degradation process. Instead, we directly learn an
iterative restoration process from low-quality and high-quality paired
examples. InDI can be applied to virtually any image degradation, given paired
training data. In conditional denoising diffusion image restoration the
denoising network generates the restored image by repeatedly denoising an
initial image of pure noise, conditioned on the degraded input. Contrary to
conditional denoising formulations, InDI directly proceeds by iteratively
restoring the input low-quality image, producing high-quality results on a
variety of image restoration tasks, including motion and out-of-focus
deblurring, super-resolution, compression artifact removal, and denoising
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