Image restoration problem is generally ill-posed, which can be alleviated by learning image prior. Inspired by the considerable performance of utilizing priors in pixel domain and wavelet domain jointly, we propose a novel transformed denoising autoencoder as prior (TDAEP). The core idea behind TDAEP is to enhance the classical denoising autoencoder (DAE) via transform domain, which captures complementary information from multiple views. Specifically, 1-level nonorthogonal wavelet coefficients are used to form 4-channel feature images. Moreover, a 5-channel tensor is obtained by stacking the original image under the pixel domain and 4-channel feature images under the wavelet domain. Then we train the transformed DAE (TDAE) with the 5-channel tensor as the network input. The optimized image prior is obtained based on the trained autoencoder, and it is incorporated into an iterative restoration procedure with the aid of the auxiliary variable technique. The resulting model is affiliationed by proximal gradient descent technique. Numerous experiments demonstrated that the TDAEP outperforms a set of image restoration benchmark algorithms.

图像恢复问题通常是不适的，可以通过事先学习图像来缓解。受像素域和小波域联合利用先验的可观性能的启发，我们提出了一种新颖的先验变换变换去噪自动编码器（TDAEP）。TDAEP的核心思想是通过变换域来增强经典的降噪自动编码器（DAE），该变换域可以从多个视图中捕获互补信息。具体地，使用1级非正交小波系数来形成4通道特征图像。此外，通过堆叠像素域下的原始图像和小波域下的4通道特征图像，可以获得5通道张量。然后，我们使用5通道张量作为网络输入来训练变换后的DAE（TDAE）。根据训练有素的自动编码器获得优化的图像先验，并借助辅助变量技术将其合并到迭代恢复过程中。结果模型通过近端梯度下降技术隶属。大量实验表明TDAEP的性能优于一组图像恢复基准算法。