# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 6 Conclusions  
In this study, we presented a blind deblurring variational neural network based on algorithm unfolding. This innovative approach combines the learning capabilities of neural networks with the fundamental principles of variational models, significantly enhancing model interpretability and making the iterative process more transparent and understandable. Our research demonstrates that even with limited parameters and datasets, the proposed method achieves remarkable performance. By incorporating the \(L_{0}\) regularizer and attention mechanisms, we improved the model's efficiency in image restoration and feature selection, thus enhancing its generalization ability and robustness against noise. By comparing various experimental results, we demonstrate the model's excellent performance across multiple aspects. This study not only provides new perspectives and methods for the deep learning field but also offers a more efficient, accurate, and interpretable solution to complex deblurring challenges, highlighting its significant potential and value in practical applications. Because the classification training strategy is used to train different blur types separately, the results are good in static image processing. However, the blur type of the real-time image is variable and unpredictable, resulting in the restoration effect being unstable, and there are still bottlenecks in the processing process. Therefore, we are seeking the optimal solution to further improve the deblurring performance in more complex scenes.  
To further validate the model's adaptability, we plan to explore additional real-world datasets in future work for a more comprehensive assessment. We also intend to integrate various regularization methods into the variational neural network, focusing on tasks like defocus kernel estimation in satellite images. Additionally, we aim to incorporate relevant prior knowledge, such as geographical and spatial resolution data, to enhance its application in the aerospace field. The model will also be tested on complex data from autonomous driving to further assess its applicability. We hope this work will inspire new directions in image processing research and its applications.  
**Author Contributions:** Conceptualization: S.G.; methodology: S.G.; software: S.G.; validation: S.G.; formal analysis: S.G.; investigation: S.G.; resources: S.G.; data curation: S.G.; writing--original draft preparation: S.G.; writing--review and editing: S.G.; visualization: S.G.; supervision, G.Y., Y.W., W.W., J.Z. and Z.P.; project administration: Z.P. and J.Z.; funding acquisition: Z.P. and J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant Nos. 62172247 and 12472040.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.