Learning Discriminative Shrinkage Deep Networks for Image Deconvolution

Pin Hung Kuo, Jinshan Pan, Shao Yi Chien, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing a variety of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. This paper proposes an effective non-blind deconvolution approach by learning discriminative shrinkage functions to model these terms implicitly. Most existing methods use deep convolutional neural networks (CNNs) or radial basis functions to learn the regularization term simply. In contrast, we formulate both the data term and regularization term and split the deconvolution model into data-related and regularization-related sub-problems according to the alternating direction method of multipliers. We explore the properties of the Maxout function and develop a deep CNN model with Maxout layers to learn discriminative shrinkage functions, which directly approximates the solutions of these two sub-problems. Moreover, the fast-Fourier-transform-based image restoration usually leads to ringing artifacts. At the same time, the conjugate-gradient-based approach is time-consuming; we develop the Conjugate Gradient Network to restore the latent clear images effectively and efficiently. Experimental results show that the proposed method performs favorably against the state-of-the-art methods in terms of efficiency and accuracy. Source codes, models, and more results are available at https://github.com/setsunil/DSDNet.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022 - 17th European Conference, Proceedings
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783031197994
Publication statusPublished - 2022
Event17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Duration: 2022 Oct 232022 Oct 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13679 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th European Conference on Computer Vision, ECCV 2022
CityTel Aviv

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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