38 citations · 62 across the 5 of their papers we have counts for
5 papers
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset
Benoit Brummer, Christophe De Vleeschouwer
This paper introduces the Raw Natural Image Noise Dataset (RawNIND), a diverse collection of paired raw images designed to support the development of denoising models that generali…
On the Importance of Denoising when Learning to Compress Images
Benoit Brummer, Christophe De Vleeschouwer
Image noise is ubiquitous in photography. However, image noise is not compressible nor desirable, thus attempting to convey the noise in compressed image bitstreams yields sub-par…
End-to-end optimized image compression with competition of prior distributions
Benoit Brummer, Christophe De Vleeschouwer
Convolutional autoencoders are now at the forefront of image compression research. To improve their entropy coding, encoder output is typically analyzed with a second autoencoder t…
Adapting JPEG XS gains and priorities to tasks and contents
Benoit Brummer, Christophe De Vleeschouwer
Most current research in the domain of image compression focuses solely on achieving state of the art compression ratio, but that is not always usable in today's workflow due to th…
Natural Image Noise Dataset
Benoit Brummer, Christophe De Vleeschouwer
Convolutional neural networks have been the focus of research aiming to solve image denoising problems, but their performance remains unsatisfactory for most applications. These ne…