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Underwater image super-resolution using multi-stage information distillation networks.
- Source :
-
Journal of Visual Communication & Image Representation . May2021, Vol. 77, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements. • A information distillation network is proposed for underwater image super-resolution. • A recursive residual module is constructed for informative features distillation. • A channel-wise interaction mechanism is proposed to generate distilled features. • A novel underwater image super-resolution dataset is presented. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 77
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 150361359
- Full Text :
- https://doi.org/10.1016/j.jvcir.2021.103136