Dong Jiangxin, Roth Stefan, Schiele Bernt
IEEE Trans Pattern Anal Mach Intell. 2022 Dec;44(12):9960-9976. doi: 10.1109/TPAMI.2021.3138787. Epub 2022 Nov 7.
We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale cascaded feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with simulated Gaussian noise, saturated pixels, or JPEG compression artifacts as well as real-world images. Moreover, we present detailed analyses of the benefit of the feature-based Wiener deconvolution and of the multi-scale cascaded feature refinement as well as the robustness of the proposed approach. Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin.
我们提出了一种简单有效的非盲图像去模糊方法，该方法结合了经典技术和深度学习。与在标准图像空间中直接对图像进行去模糊的现有方法不同，我们建议通过将经典的维纳反卷积框架与学习到的深度特征相结合，在特征空间中执行显式反卷积过程。然后，一个多尺度级联特征细化模块从反卷积后的深度特征中预测去模糊后的图像，逐步恢复细节和小尺度结构。所提出的模型以端到端的方式进行训练，并在具有模拟高斯噪声、饱和像素或JPEG压缩伪像的场景以及真实世界图像上进行评估。此外，我们还对基于特征的维纳反卷积和多尺度级联特征细化的优势以及所提出方法的鲁棒性进行了详细分析。我们广泛的实验结果表明，所提出的深度维纳反卷积网络能够促进去模糊结果，显著减少伪像，并且在定量上大大优于现有的非盲图像去模糊方法。
