NBD-GAP: Non-Blind Image Deblurring without Clean Target Images
NBD-GAP: Non-Blind Image Deblurring without Clean Target Images
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DOI：
10.1109/icip46576.2022.9897543
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Nithin Gopalakrishnan Nair;R. Yasarla;Vishal M. Patel
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文献类型：
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作者：
Nithin Gopalakrishnan Nair;R. Yasarla;Vishal M. Patel
近年来，基于深度神经网络的复原方法在各种图像去模糊任务中取得了最好的效果。然而，基于深度学习的去模糊网络的一个主要缺点是需要大量模糊-干净的图像对进行训练才能获得良好的性能。此外，当测试过程中的模糊图像和模糊核与训练过程中使用的图像和核非常不同时，深度网络往往无法很好地执行。这主要是由于训练数据上的网络参数过拟合造成的。在这项工作中，我们提出了一种解决这些问题的方法。我们将非盲图像去模糊问题归结为一个去噪问题。为此，我们对具有相应模糊核的一对模糊图像执行维纳滤波。这会产生一对带有有色噪声的图像。因此，去模糊问题转化为去噪问题。然后，在不使用显式清洁目标图像的情况下，解决了去噪问题。大量的实验表明，我们的方法取得了与最先进的非盲去模糊工作相当的结果。
In recent years, deep neural network-based restoration methods have achieved state-of-the-art results in various image deblurring tasks. However, one major drawback of deep learning-based deblurring networks is that large amounts of blurry-clean image pairs are required for training to achieve good performance. Moreover, deep networks often fail to perform well when the blurry images and the blur kernels during testing are very different from the ones used during training. This happens mainly because of the overfitting of the network parameters on the training data. In this work, we present a method that addresses these issues. We view the non-blind image deblurring problem as a denoising problem. To do so, we perform Wiener filtering on a pair of blurry images with the corresponding blur kernels. This results in a pair of images with colored noise. Hence, the deblurring problem is translated into a denoising problem. We then solve the denoising problem without using explicit clean target images. Extensive experiments are conducted to show that our method achieves results that are on par to the state-of-the-art non-blind deblurring works.