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Image deblurring method based on self-attention and residual wavelet transform
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AI
DOI：10.1016/j.eswa.2023.123005.png)
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Abstract
En 中文
The restoration technology of non-uniform blurred images is a challenging open topic. Most of the existing algorithms fail to effectively fuse multi-scale feature extraction with a self-attention mechanism, and also ignore the potential contribution of image frequency domain information to image restoration. Frequency domain features play an important role in restoring high-quality images and ignoring this property often leads to over-smoothing of the restoration results. In response to these problems, an image deblurring method based on self-attention and residual wavelet transform is proposed in this paper. Based on a single U-Net network, the multi-scale feature cross-fusion strategy and self-attention mechanism are combined to make the network pay more attention to different degrees of blurred regions so that relatively robust blurred features can be extracted for image deblurring. Meanwhile, considering the important role of frequency domain features for image restoration, the wavelet transform is embedded into the depth residual network to convert spatial domain features to wavelet domain, and the sharp details such as edge contours of blurred features are restored by making full use of the texture structure information possessed by high-frequency sub-bands, which further improves the image restoration performance. Experimental results of quantitative and qualitative comparison with other state-of-the-art methods show that the image deblurring effect of the proposed network performs favorably in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics. The codes and models are available at https://github.com/BingY998/MRDNet.
Keywords:
Image deblurring
Feature cross fusion
Self-attention mechanism
Wavelet transform
Residual network
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