SCPA-Net: Self-calibrated pyramid aggregation for image dehazing

Zhihua Chen, Yu Zhou, Ran Li, Ping Li, Bin Sheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Dehazing as an important image processing field has developed for many years, there exist many excellent methods for exploring more complex networks to solve this problem. In this paper, instead of designing a complex network structure, we propose a novel dehazing network based on the consideration of enhancing feature aggregation and feature representation abilities of dehazing architecture. Specifically, we propose a self-calibrated pyramid aggregation network (SCPA-Net) for image dehazing, which is based on an encoder-decoder architecture. In the encoder, we build a self-attention block as a unit to aggregate information from a neighborhood to adapt to its content. In the decoder, we introduce a self-calibration block to capture long-range spatial and channel dependencies to produce more discriminative representations. Finally, to learn the scale information, the pyramid upsampling structure is applied to aggregate the multiscale self-calibrated attentive features. Experimental results show our SCPA-Net can achieve impressive dehazing performance.

Original languageEnglish
Article numbere2061
Pages (from-to)1-12
JournalComputer Animation and Virtual Worlds
Volume33
Issue number3-4
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • image dehazing
  • pyramid upsampling structure
  • self-attention
  • self-calibration

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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