WavCycleGAN

1. Unsupervised Denoising for Satellite Imagery
using Wavelet Directional CycleGAN
Joonyoung Song, Jae-Heon Jeong, Dae-Soon Park, Hyun-Ho Kim, Doo-Chun Seo, and Jong Chul Ye
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Joonyoung Song
Joonyoung Song, Jae-Heon Jeong, Dae-Soon Park, Hyun-Ho Kim, Doo-Chun Seo, Jong Chul Ye, "Unsupervised Denoising for Satellite Imagery using Wavelet Directional CycleGAN",
IEEE Trans. on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6823-6839, Aug. 2021,

2. Denoising for Satellite Imagery
 Goal: Denoising Satellite images (unsupervised manner)
 Vertical stripe noise
 Horizontal wave noise
Vertical stripe noise
Horizontal wave noise
Goal
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3. Denoising for Satellite Imagery
 CycleGAN in the image domain
 Failed to remove noise patterns
 Edges and details are blurred
Input CycleGAN Target(model-method)
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4. Denoising for Satellite Imagery
 CycleGAN in the image domain
 Failed to remove noise patterns
 Edges and details are blurred
CycleGAN
Input Target(model-based method)
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5. Denoising for Satellite Imagery
 Property of target noises
 Directional components: Vertical / Horizontal
-> prior knowledge
 Wavelet transform
 A wavelet transform is a linear transformation in which the basis functions are scaled and
shifted versions of one function, called the “mother wavelet.”
 Extract directional components of images -> leads to design effective denoising method
Wavelet
Transform
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6.  Wavelet Directional Image
 We use wavelet directional images that are obtained from subset of wavelet
bands containing noises.
 By using wavelet directional image, we can reconstruct only detail bands
without sacrificing non-noise components
Denoising for Satellite Imagery
WT: wavelet transform
IWT: inverse wavelet transform
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7.  Wavelet Directional Image
 We use wavelet directional images that are obtained from subset of wavelet
bands containing noises.
 By using wavelet directional image, we can reconstruct only detail bands
without sacrificing non-noise components
Denoising for Satellite Imagery
WT: wavelet transform
IWT: inverse wavelet transform
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8.  WavCycleGAN (CycleGAN in the wavelet directional image domain)
 Using wavelet subband images, we trained CycleGAN for denoising.
 y: noisy, x: clean
 GAN loss, cyclic loss, and identity loss
Denoising for Satellite Imagery
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9.  Denoising method for the vertical stripe noise
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10.  Denoising method for the horizontal wave noise
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11. Input BM3D BayesShrink WNNM
GAN CycleGAN WavCycleGAN(Ours) Target(Conventional
method)
 Denoising results for the vertical stripe noise (Real noise)
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12.  Denoising results for the vertical stripe noise (Synthetic noise)
GAN CycleGAN WavCycleGAN
(Ours)
WNNM
Noisy BM3D BayesShrink
Clean
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13.  Denoising results for the horizontal wave noise (Real noise)
BM3D Bayes Shrink WNNM GAN_RGBN
CycleGAN_G WavCycleGAN_G CycleGAN_RGBN WavCycleGAN
(RGBN)
Input
Target
(Conventional method)
Residual images
Residual images
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14.  Denoising results for the horizontal wave noise (Synthetic noise)
Clean BM3D BayesShrink WNNM GAN CycleGAN WavCycleGAN
(Ours)
Noisy
Residual images
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15. Wavelet directional image
 Ablation study: which wavelet?
 The characteristics of wavelet coefficients vary depending on the type of wavelet (mother
wavelet)
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16. Wavelet directional image
 Ablation study: which wavelet?
Db3 wavelet
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17. Wavelet directional image
 Ablation study: decomposition level
 The level of wavelet decomposition affects the performance of noise removal.
 With the ideal wavelet decomposition level, the noise signal is presented in
certain bands
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18. Wavelet directional image
 Ablation study: decomposition level
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19. Thank you
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