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Title: Trainable Algorithms for Inverse Imaging Problems
Abstract:
Classical inverse imaging problems are often formulated as the minimization of
a cost function consisting of finding a signal that fits data observations, and
that is compatible with a priori knowledge about the solution. This approach
requires both a good physical model of the data acquisition process and a good
prior. On the other hand, when supervised data are available (e.g., pairs of
corrupted/clean signals), it is also classical to use data-driven machine
learning approaches, often a deep neural networks, which are often seen as black
boxes models that are huge-dimensional and hard to interpret. In this talk, we present a hybrid
strategy, which we call ``trainable algorithms'', that retains the
interpretability of classical inverse problem formulations, while allowing us
to train model parameters end to end.
Our first example is the problem of super-resolution from a burst of raw
low-resolution (LR) images acquired by a prosumer or smartphone camera. The
goal is to exploit image misalignments and aliasing artefacts (which contain
useful high-frequency information) in order to increase the number of available
samples from the underlying high-resolution (HR) signal. The problem is
difficult as it requires (i) accurately aligning images with subpixel accuracy,
(ii) dealing with noisy raw data produced by the sensor, and (iii) designing an
appropriate image prior. In this work, we demonstrate state-of-the-art results
on synthetic benchmarks and on real raw data produced by various digital
cameras and smartphones.
Our second example focuses on more traditional restoration tasks from a single
image in sparse coding models while also leveraging non-local self-similarity priors,
which have been shown to be powerful for image restoration problems.
The first interesting conclusion is the ability of our models to perform on par
with state-of-the-art convolutional neural networks with orders of magnitude
less parameters. The second notable fact is the ability to leverage the model
interpretability to improve the effiency of our models for blind denoising.
In these two examples, the benefits of trainable algorithms were the ability to
mix traditional inverse problem formulations with deep learning principles,
leading to robust and parameter-efficient trainable architectures
This is a joint work with Bruno Lecouat and Jean Ponce.
Since the slides contains nice looking pictures that may suffer from Zoom's
compression artefacts, you may find those more impressive by downloading the
pdf directly:
https://thoth.inrialpes.fr/people/mairal/resources/pdf/talk_trainalgs.pdf