Pith. sign in

REVIEW 1 cited by

Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1712.03400 v1 pith:NT77GDZY submitted 2017-12-09 cs.CV

Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2

classification cs.CV
keywords imagesdeepmodelconvolutionalinception-resnet-v2acceptanceapplicationsapproaches
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We review some of the most recent approaches to colorize gray-scale images using deep learning methods. Inspired by these, we propose a model which combines a deep Convolutional Neural Network trained from scratch with high-level features extracted from the Inception-ResNet-v2 pre-trained model. Thanks to its fully convolutional architecture, our encoder-decoder model can process images of any size and aspect ratio. Other than presenting the training results, we assess the "public acceptance" of the generated images by means of a user study. Finally, we present a carousel of applications on different types of images, such as historical photographs.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Deep Exemplar-based Video Colorization

    cs.CV 2019-06 unverdicted novelty 6.0

    A recurrent end-to-end network for exemplar-based video colorization that unifies semantic correspondence and color propagation with a temporal consistency loss.