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Author Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa edit   pdf
url  doi
openurl 
  Title MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution Type Conference Article
  Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 2703-2712  
  Keywords  
  Abstract Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: ($i$) to adaptively extract informative features and learn more expressive spatial context information; ($ii$) to better leverage multi-level representations before up-sampling stage; and ($iii$) to allow an efficient information and gradient flow within the network. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model. Extensive experiments show the superiority of our model against other SOTA SR approaches.  
  Address Virtual; January 2021  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes (up) MSIAU; 600.130; 600.122 Approved no  
  Call Number Admin @ si @ MAS2021b Serial 3582  
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Author Xavier Soria; Edgar Riba; Angel Sappa edit   pdf
url  doi
openurl 
  Title Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection Type Conference Article
  Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered.  
  Address Aspen; USA; March 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes (up) MSIAU; 600.130; 601.349; 600.122 Approved no  
  Call Number Admin @ si @ SRS2020 Serial 3434  
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