基于深度多级小波变换的图像盲去模糊算法

Image Blind Deblurring Algorithm Based on Deep Multi-level Wavelet Transform

近年来卷积神经网络广泛应用于单幅图像去模糊问题,卷积神经网络的感受野大小、网络深度等会影响图像去模糊算法性能。为了增大感受野以提高图像去模糊算法的性能,该文提出一种基于深度多级小波变换的图像盲去模糊算法。将小波变换嵌入编-解码结构中,在增大感受野的同时加强图像特征的稀疏性。为在小波域重构高质量图像,该文利用多尺度扩张稠密块提取图像的多尺度信息,同时引入特征融合块以自适应地融合编-解码之间的特征。此外,由于小波域和空间域对图像信息的表示存在差异,为融合这些不同的特征表示,该文利用空间域重建模块在空间域进一步提高重构图像的质量。实验结果表明该文方法在结构相似度(SSIM)和峰值信噪比(PSNR)上具有更好的性能,而且在真实模糊图像上具有更好的视觉效果。

In recent years,convolutional neural networks are widely used in single image deblurring problems.The receptive field size and network depth of convolutional neural networks can affect the performance of image deblurring algorithms.In order to improve the performance of image deblurring algorithm by increasing the receptive field,an image blind deblurring algorithm based on deep multi-level wavelet transform is proposed.Embedding the wavelet transform into the encoder-decoder architecture enhances the sparsity of the image features while increasing the receptive field.In order to reconstruct high-quality images in the wavelet domain,the paper leverges to multi-scale dilated dense block to extract multi-scale information of images,and introduces feature fusion blocks to fuse adaptively features between encoder and decoder.In addition,due to the difference in representation of image information between the wavelet domain and the spatial domain,in order to fuse these different feature representations,the spatial domain reconstruction module is used to improve further the quality of the reconstructed image in the spatial domain.The experimental results show that the proposed method has better performance on Structural SIMilarity index(SSIM)and Peak Signal-to-Noise Ratio,and has better visual effects on real blurred images.