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M. Hirsch

22 papers hereh-index 275k citations60 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author15
  • last author7

Across the 22 of 22 papers where every author was matched, so the position is known.

fields
  • cs.CV11
  • astro-ph.CO7
  • astro-ph.IM3
  • physics.optics1
same name
  • M. Hirsch — 116 papers, h 44
  • M. Hirsch — 26 papers, h 16
  • M. Hirsch — 24 papers, h 39
  • M. Hirsch — 8 papers, h 6
  • M. Hirsch — 6 papers, h 7
  • M. Hirsch — 4 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20092018
most citedThe DES Science Verification Weak Lensing Shear Catalogues

189 citations · 957 across the 19 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017

Learning Blind Motion Deblurring

Patrick Wieschollek, Michael Hirsch, Bernhard Schölkopf +1

As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot freque…

cs.CV2017★ 9 cited

Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

Waleed M. Gondal, Jan M. Köhler, René Grzeszick +2

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical expert…

cs.CV2017★ 25 cited

Online Video Deblurring via Dynamic Temporal Blending Network

Tae Hyun Kim, Kyoung Mu Lee, Bernhard Schölkopf +1

State-of-the-art video deblurring methods are capable of removing non-uniform blur caused by unwanted camera shake and/or object motion in dynamic scenes. However, most existing me…

cs.CV2017★ 15 cited

Discriminative Transfer Learning for General Image Restoration

Lei Xiao, Felix Heide, Wolfgang Heidrich +2

Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational effi…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.