Demosaicking and denoising are the key first stages of the digital imaging pipeline but they
are also a severely ill-posed problem that infers three color values per pixel from a single …

Image denoising, demosaicing and super-resolution are key problems of image restoration
well studied in the recent decades. Often, in practice, one has to solve these problems …

Usually located at the very early stages of the computational photography pipeline,
demosaicing and denoising play important parts in the modern camera image processing …

We propose a new definition for the gradient field of a discrete image defined on a twice
finer grid. The differentiation process from an image to its gradient field is viewed as the …

We explore the use of the recently proposed'total nuclear variation'(TV N) as a regularizer for
reconstructing multi-channel, spectral CT images. This convex penalty is a natural extension …

High levels of noise usually exist in today's captured images due to the relatively small
sensors equipped in the smartphone cameras, where the noise brings extra challenges to …

This paper introduces deformable convolution in deep learning based joint denoising and
demosaicing (JDD), which yields more adaptable representation and larger receptive fields …

We propose new optimization algorithms to minimize a sum of convex functions, which may
be smooth or not and composed or not with linear operators. This generic formulation …

Demosaicking and denoising are the first steps of any camera image processing pipeline
and are key for obtaining high quality RGB images. A promising current research trend aims …

We introduce a machine learning approach to demosaicing, the reconstruction of color
images from incomplete color filter array samples. There are two challenges to overcome by …