Abstract
Modern inexpensive imaging sensors suffer from inherent hardware constraints which often result in captured images of poor quality. Among the most common ways to deal
with such limitations is to rely on burst photography, which
nowadays acts as the backbone of all modern smartphone
imaging applications. In this work, we focus on the fact that
every frame of a burst sequence can be accurately described
by a forward (physical) model. This, in turn, allows us to
restore a single image of higher quality from a sequence
of low-quality images as the solution of an optimization
problem. Inspired by an extension of the gradient descent
method that can handle non-smooth functions, namely the
proximal gradient descent, and modern deep learning techniques, we propose a convolutional iterative network with a
transparent architecture. Our network uses a burst of lowquality image frames and is able to produce an output of
higher image quality recovering fine details which are not
distinguishable in any of the original burst frames. We focus both on the burst photography pipeline as a whole, i.e.,
burst demosaicking and denoising, as well as on the traditional Gaussian denoising task. The developed method
demonstrates consistent state-of-the art performance across
the two tasks and as opposed to other recent deep learning
approaches does not have any inherent restrictions either to
the number of frames or their ordering.