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fix markdown typos

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@@ -23,8 +23,7 @@ We first validated that NAFNet trained on deadleaves performed well on the blind
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  | Qualitative results at SNR in = 20dB | Quantitative results
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  | :---: | :---: |
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- | ![](https://huggingface.co/spaces/balthou/image-deblurring/resolve/main/illustrationsdeadleaves_vs_natural_with_psnr.png) | ![](
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- https://huggingface.co/spaces/balthou/image-deblurring/resolve/main/illustrations/cross_val_natural.png)
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  Finally, when applying the deadleaves training to the blind deblurring problem, one of the advantage we have notticed is that the network always tries to deblur even when the level of blur is high. On the contrary, when trained on natural images, the NAFNEt does not work so well when the blur level is too big.
 
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  | Qualitative results at SNR in = 20dB | Quantitative results
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  | :---: | :---: |
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+ | ![](https://huggingface.co/spaces/balthou/image-deblurring/resolve/main/illustrations/deadleaves_vs_natural_with_psnr.png) | ![](https://huggingface.co/spaces/balthou/image-deblurring/resolve/main/illustrations/cross_val_natural.png)
 
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  Finally, when applying the deadleaves training to the blind deblurring problem, one of the advantage we have notticed is that the network always tries to deblur even when the level of blur is high. On the contrary, when trained on natural images, the NAFNEt does not work so well when the blur level is too big.