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Poisson image denoising based on fractional-order total variation

  • * Corresponding author: Yifei Lou

    * Corresponding author: Yifei Lou
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  • Poisson noise is an important type of electronic noise that is present in a variety of photon-limited imaging systems. Different from the Gaussian noise, Poisson noise depends on the image intensity, which makes image restoration very challenging. Moreover, complex geometry of images desires a regularization that is capable of preserving piecewise smoothness. In this paper, we propose a Poisson denoising model based on the fractional-order total variation (FOTV). The existence and uniqueness of a solution to the model are established. To solve the problem efficiently, we propose three numerical algorithms based on the Chambolle-Pock primal-dual method, a forward-backward splitting scheme, and the alternating direction method of multipliers (ADMM), each with guaranteed convergence. Various experimental results are provided to demonstrate the effectiveness and efficiency of our proposed methods over the state-of-the-art in Poisson denoising.

    Mathematics Subject Classification: Primary: 65F22, 65Y04; Secondary: 52A41, 49N45.


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  • Figure 1.  A synthetic example. Top: the synthetic ground-truth image and two noisy images with peak values 55 and 255, respectively. Bottom: FOTV Poisson denoising results (via Algorithm 3) with the respective fractional order $ 1 $, $ 1.8 $, and $ 2.4 $ for the case with peak value 255

    Figure 2.  Algorithmic comparison in terms of energy (left) and PSNR (right) by denoising the noisy synthetic image with peak value of 255 (top) and the Barbara image with peak value of 55 (bottom)

    Figure 3.  PSNR versus fractional orders for the synthetic image (top) and Barbara image (bottom) with peak values at 55 (left) and 255 (right)

    Figure 4.  Poisson denoising results of Train image with peak at 55

    Figure 5.  Poisson denoising results of Barbara image with peak at 255

    Figure 6.  Poisson denoising results of Mandril image with peak at 55

    Figure 7.  Poisson denoising results of Penguin image with peak at 155 and Cameraman image with peak at 255

    Table 1.  Poisson denoising comparison. Each entry contains PSNR and SSIM values. The best results are highlighted in bold

    Test Image Peak Noisy NPTool NLPCA BM3D Proposed
    Cameraman 55 20.63 27.81/0.87 27.36/0.85 28.80/0.89 28.15/0.87
    155 25.18 30.65/0.91 29.52/0.87 29.06/0.90 30.90/0.92
    255 27.38 32.06/0.93 30.58/0.91 29.45/0.90 32.31/0.94
    Penguin 55 19.53 30.86/0.91 30.49/0.89 31.50/0.92 31.46/0.92
    155 24.01 33.34 /0.94 32.14/0.91 32.37/0.93 33.86/0.95
    255 26.74 34.27/0.95 33.21/0.93 32.69/0.93 34.73/0.96
    Train 55 20.94 28.04/0.88 27.20/0.81 28.01/0.89 28.25/0.88
    155 25.47 30.99 /0.92 29.64/0.88 28.31/0.89 31.06/0.92
    255 27.85 32.38/0.94 30.76/0.88 29.16/0.90 32.43/0.94
    Mandril 55 19.75 22.57/0.76 22.00 /0.71 20.39/0.56 22.80/0.76
    155 24.26 25.73/0.86 25.06/0.84 20.58/0.58 26.00/0.87
    255 26.46 27.67/0.90 26.46/0.87 20.95/0.60 27.76/0.91
    Barbara 55 20.57 25.48/0.81 27.97/0.86 29.44/0.91 25.87/0.81
    155 25.14 28.52/0.87 30.29/0.90 30.77/0.93 29.14/0.89
    255 27.31 30.14/0.91 31.01/0.92 31.47/0.94 30.78/0.92
     | Show Table
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    Table 2.  Computation time (in sec)

    Test image(size) Peak NPTool NLPCA BM3D Proposed
    Barbara (512 × 512) 55 5.68 72.73 2.26 8.68
    Train (512 × 357) 155 5.49 91.70 1.63 6.36
    Mandril (256 × 256) 255 1.99 3.77 0.71 1.32
     | Show Table
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