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Learning Enriched Features for Real Image Restoration and Enhancement
Lecture Notes in Computer Science
  • Syed Waqas Zamir, Inception Institute of Artificial Intelligence
  • Aditya Arora, Inception Institute of Artificial Intelligence
  • Salman Khan, Inception Institute of Artificial Intelligence & Mohamed bin Zayed University of Artificial Intelligence
  • Munawar Hayat, Inception Institute of Artificial Intelligence & Mohamed bin Zayed University of Artificial Intelligence
  • Fahad Shabaz Khan, Inception Institute of Artificial Intelligence & Mohamed bin Zayed University of Artificial Intelligence
  • Ming-Hsuan Yang, University of California, Merced, United States & Google Research, Merced, United States
  • Ling Shao, Inception Institute of Artificial Intelligence & Mohamed bin Zayed University of Artificial Intelligence
Document Type
Conference Proceeding
Abstract

With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography and medical imaging. Recently, convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. Existing CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatially precise but contextually less robust results are achieved, while in the latter case, semantically reliable but spatially less accurate outputs are generated. In this paper, we present an architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network and receiving strong contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention based multi-scale feature aggregation. In a nutshell, our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on five real image benchmark datasets demonstrate that our method, named as MIRNet, achieves state-of-the-art results for image denoising, super-resolution, and image enhancement. The source code and pre-trained models are available at https://github.com/swz30/MIRNet. © 2020, Springer Nature Switzerland AG.

DOI
10.1007/978-3-030-58595-2_30
Publication Date
10-20-2020
Keywords
  • Color photography,
  • Computer vision,
  • Convolution,
  • Convolutional neural networks,
  • Image denoising,
  • Image enhancement,
  • Medical imaging,
  • Restoration,
  • Attention mechanisms,
  • Benchmark datasets,
  • Computational photography,
  • Contextual information,
  • Conventional approach,
  • High quality images,
  • Information exchanges,
  • Multi-scale features,
  • Image reconstruction
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Citation Information
S.W. Zamir, et al, "Learning Enriched Features for Real Image Restoration and Enhancement", in Computer Vision – ECCV 2020, in Lecture Notes in Computer Science, vol 12370, Oct. 2020, pp. 492-511, doi.org:10.1007/978-3-030-58595-2_30