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FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising.

Kai Zhang, Wangmeng Zuo, Lei Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 12, 2018
    PubMed
    Summary

    A new deep learning model, FFDNet, efficiently denoises images across various noise levels using a single network. This flexible approach handles spatially variant noise and offers faster performance than existing methods for practical applications.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Discriminative learning methods are effective for image denoising but typically require separate models for each noise level.
    • Existing methods lack flexibility for spatially variant noise, limiting practical applications.
    • Current approaches often necessitate multiple models, increasing complexity and reducing efficiency.

    Purpose of the Study:

    • To introduce a fast and flexible denoising convolutional neural network (FFDNet) capable of handling a wide range of noise levels with a single model.
    • To enable the removal of spatially variant noise through a tunable noise level map input.
    • To achieve a balance between inference speed and denoising performance for practical image restoration.

    Main Methods:

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  • Developed a novel denoising convolutional neural network, FFDNet, utilizing a tunable noise level map as input.
  • Implemented FFDNet to operate on downsampled subimages, optimizing the trade-off between speed and performance.
  • Trained and evaluated FFDNet on synthetic and real noisy image datasets.

    • Main Results:

    • FFDNet effectively denoises images across a broad spectrum of noise levels (0-75) using a single network.
    • The network demonstrated the capability to remove spatially variant noise by utilizing non-uniform noise level maps.
    • FFDNet achieved faster inference speeds compared to the benchmark BM3D, even on CPU, without compromising denoising quality.

    Conclusions:

    • FFDNet presents a highly effective and efficient solution for image denoising.
    • The network's flexibility in handling diverse noise conditions and its speed make it attractive for real-world denoising tasks.
    • FFDNet overcomes limitations of previous discriminative methods, offering a versatile tool for image restoration.