# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 6 Conclusions  
In this paper, we propose a real image deblurring network framework, IDR\({}^{2}\)ENet, based on reblurring to estimate the implicit degradation representation. Unlike the general methods for estimating explicit degradation representations, IDR\({}^{2}\)ENet learns implicit degradation representations by constructing a \"sharp image-blurred image\" reblurring process, and uses the generated degradation representations to guide the deblurring and reblurring processes. In order to better constrain the feature similarity between the reblurred image and the original blurred image, a perceptual loss function is added to the corresponding loss function, and SSIM is introduced to calculate the difference between the deblurred image and the original blurred image. The experimental results show that our network achieves stable and efficient deblurring results for real image deblurring on the RealBlur dataset, RWBI dataset and real captured blurred images. Additionally, IDR\({}^{2}\)ENet has better results and lower network complexity compared with other methods.  
Conceptualization, Z.Z., H.G., M.Q., Z.W. and C.R.; methodology, Z.Z., H.G., M.Q., Z.W. and C.R.; software, Z.Z., M.Q. and C.R.; validation, Z.Z., H.G., M.Q., Z.W. and C.R.; formal analysis, Z.Z., H.G., M.Q., Z.W. and C.R.; investigation, Z.Z., H.G., M.Q., Z.W. and C.R.; resources, M.Q. and C.R.; data curation, H.G., M.Q. and Z.W.; writing--original draft preparation, Z.Z. and M.Q.; writing--review and editing, Z.Z., M.Q. and C.R.; visualization, Z.Z., H.G., M.Q., Z.W. and C.R.; supervision, Z.W. and C.R.; project administration, Z.W. and C.R.; funding acquisition, Z.W. and C.R. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Network structure of IDR\({}^{2}\)ENet-R.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 62171304, and the Key Research and Development Project of Sichuan Province under Grant 2022 YFS00989.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset GoPro can be downloaded from [[https://seungjunnah.github.io/Datasets/gopro](https://seungjunnah.github.io/Datasets/gopro)]([https://seungjunnah.github.io/Datasets/gopro](https://seungjunnah.github.io/Datasets/gopro)) (accessed on 7 December 2016). The dataset RealBlur can be downloaded from [[http://cg.postech.ac.kr/research/realblur/](http://cg.postech.ac.kr/research/realblur/)]([http://cg.postech.ac.kr/research/realblur/](http://cg.postech.ac.kr/research/realblur/)) (accessed on 24 August 2020). The dataset RWBI can be downloaded from [[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) fHkPiZOvLQSC4 HhT8-wA6 dh0M4]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) fHkPiZOvLQSC4 HhT8-wA6 dh0M4) skpTMi/view (accessed on 4 April 2020).  
**Acknowledgments:** The authors would like to thank the National Natural Science Foundation of China for the support through Grant 62171304, and the Key Research and Development Project of Sichuan Province for the support through Grant 2022 YFS00989.  
**Conflicts of Interest:** The authors declare no conflict of interest.