Pith. sign in

REVIEW 1 cited by

LSD$_2$ -- Joint Denoising and Deblurring of Short and Long Exposure Images with CNNs

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 1811.09485 v3 pith:OGTDLOBG submitted 2018-11-23 cs.CV

classification cs.CV
keywords exposureimagesdeblurringdenoisingapproachconditionsexistinghigh-quality
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The paper addresses the problem of acquiring high-quality photographs with handheld smartphone cameras in low-light imaging conditions. We propose an approach based on capturing pairs of short and long exposure images in rapid succession and fusing them into a single high-quality photograph. Unlike existing methods, we take advantage of both images simultaneously and perform a joint denoising and deblurring using a convolutional neural network. A novel approach is introduced to generate realistic short-long exposure image pairs. The method produces good images in extremely challenging conditions and outperforms existing denoising and deblurring methods. It also enables exposure fusion in the presence of motion blur.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

    cs.CV 2019-08 conditional novelty 6.0 of 10

    DeblurGAN-v2 uses a feature pyramid network and a relativistic GAN to deblur images 10 to 100 times faster than prior methods while staying competitive in quality.

Pith tools