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High-Frequency aware Perceptual Image Enhancement

arXiv 2105.11711 ↗First posted 25 May 2021cs.CV eess.IVcited by 0

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Abstract (arXiv)

In this paper, we introduce a novel deep neural network suitable for multi-scale analysis and propose efficient model-agnostic methods that help the network extract information from high-frequency domains to reconstruct clearer images. Our model can be applied to multi-scale image enhancement problems including denoising, deblurring and single image super-resolution. Experiments on SIDD, Flickr2K, DIV2K, and REDS datasets show that our method achieves state-of-the-art performance on each task. Furthermore, we show that our model can overcome the over-smoothing problem commonly observed in existing PSNR-oriented methods and generate more natural high-resolution images by applying adversarial training.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 27 references that are in the map. First posted on arXiv 25 May 2021.

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23 of 27 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  5. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  6. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  7. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  8. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  9. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  10. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 903
  11. 2018Residual Dense Network for Image Super-Resolutioncited by 799
  12. 2016Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurringcited by 727
11 more
  1. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 510
  2. 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 362
  3. 2018Scale-recurrent Network for Deep Image Deblurringcited by 333
  4. 2019Variational Information Distillation for Knowledge Transfercited by 324
  5. 2017MemNet: A Persistent Memory Network for Image Restorationcited by 244
  6. 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 195
  7. 2020Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolutioncited by 90
  8. 2020Learning with Privileged Information for Efficient Image Super-Resolutioncited by 53
  9. 2020Perceptual Extreme Super Resolution Network with Receptive Field Blockcited by 15
  10. 2020FAN: Frequency Aggregation Network for Real Image Super-resolutioncited by 10
  11. 2019Image Formation Model Guided Deep Image Super-Resolutioncited by 2

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