Real-time Controllable Denoising for Image and Video

• URL: http://arxiv.org/abs/2303.16425v1
• Date: Wed, 29 Mar 2023 03:10:28 GMT
• Title: Real-time Controllable Denoising for Image and Video
• Authors: Zhaoyang Zhang, Yitong Jiang, Wenqi Shao, Xiaogang Wang, Ping Luo, Kaimo Lin, Jinwei Gu
• Abstract summary: Controllable image denoising aims to generate clean samples with human priors and balance sharpness and smoothness. We introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline. RCD provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference.
• Score: 44.68523669975698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inference each time, making it almost impossible for real-time user interaction. In this paper, we introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline that provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference. Unlike existing controllable denoising methods that require multiple denoisers and training stages, RCD replaces the last output layer (which usually outputs a single noise map) of an existing CNN-based model with a lightweight module that outputs multiple noise maps. We propose a novel Noise Decorrelation process to enforce the orthogonality of the noise feature maps, allowing arbitrary noise level control through noise map interpolation. This process is network-free and does not require network inference. Our experiments show that RCD can enable real-time editable image and video denoising for various existing heavy-weight models without sacrificing their original performance.

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