High-Frequency aware Perceptual Image Enhancement

• URL: http://arxiv.org/abs/2105.11711v1
• Date: Tue, 25 May 2021 07:33:14 GMT
• Title: High-Frequency aware Perceptual Image Enhancement
• Authors: Hyungmin Roh and Myungjoo Kang
• Abstract summary: We introduce a novel deep neural network suitable for multi-scale analysis and propose efficient model-agnostic methods. Our model can be applied to multi-scale image enhancement problems including denoising, deblurring and single image super-resolution.
• Score: 0.08460698440162888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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