# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation  
[PERSON]  
1 College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China; [EMAIL_ADDRESS]. which leads to the estimation of a single blurring kernel that cannot be well applied to real-world image deblurring. To address this problem, scholars have proposed a series of end-to-end methods for learning the mapping relationship between blurred and sharp images [8; 9; 10], which are mostly based on deep learning networks, such as Convolutional Neural Networks (CNN) [11; 12; 13; 14; 15] and Generative Adversarial Networks (GAN) [10; 16; 17; 18]. Among CNN-based works, better results have been achieved in recent years based on Deep Auto-Encoders (DAE), which fuse U-Net network structures [17; 19; 20]. [PERSON] et al. [20] set up an a priori face parsing/segmentation network to predict face labels before U-Net, and then the blurred images were fed into U-Net along with the predicted face labels to obtain the deblurred images. Other approaches analyze multiple DAEs and U-Nets in an attempt to construct cascade networks, where one U-Net produces a coarse deblurred image and then feeds into the second U-Net to obtain better deblurring performance. Among the GAN-based approaches, [PERSON] et al. [8] were the first to introduce the adversarial loss function \(\mathcal{L}_{adv}\). They then constructed an eleven-layer discriminator, which is trained with real sharp images as the input and computes \(\mathcal{L}_{adv}\) based on whether it can eventually distinguish deblurred images from real sharp images. Subsequent GAN-based approaches basically follow this idea [10; 16; 21]: the generator \(G\) generates a deblurred image \(x_{de}\), and the training is considered to be finished if \(G\) fools the discriminator \(D\) so that it cannot distinguish between the generated image \(x_{de}\) and the real sharp image \(x\). [PERSON] et al. [10; 16] proposed DeblurGAN, whose generator consists of two-stride layers of convolution blocks, nine layers of residual blocks and two transposed convolution blocks. The DeblurGANv2 proposed on this basis introduces the results of relativistic conditional GAN [22], whose generator uses a pyramidal-feature architecture, while its discriminator uses the Double-Scale RaGAN-LS Discriminator, thus improving the efficiency and performance of the whole network. However, whether U-Net or GAN, the end-to-end-based learning methods mentioned above lack the exploitation of image degradation representations such that their performance in real-world deblurring tasks is still limited. In addition, blurred regions in blurred images usually show greater variation than noisy points or high-frequency texture details, so the learning and estimation of the degradation process is important for better reconstruction. Based on the above issues, and inspired by the work of [PERSON] et al. [23], [PERSON] et al. [24], [PERSON] et al. [25] and [PERSON] et al. [26] on image restoration, we propose a real image deblurring network based on the implicit degradation representations and reblur estimation with an encoder-decoder structure, called IDR\({}^{2}\)ENet. More specifically, the network framework contains three main processes, degradation estimation, reblurring, and deblurring, which consist of a degradation estimation subnetwork, a multi-scale degradation-representation-guided deblurring subnetwork and a multi-scale degradation-representation-guided reblurring subnetwork, respectively. The main contributions of this paper can be summarized as follows:  
* We propose an implicit degradation representation and reblur estimation network called IDR\({}^{2}\)ENet. The network learns and estimates implicit degradation representations in real images by reblurring sharp images (generating a reblurred image from a real sharp image that resembles a real blurred image). The degradation representations are then used to guide the deblurring process for better reconstruction.