# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions  
The experiment was carried out using the PyTorch framework on a machine equipped with an NVIDIA GeForce RTX 3090 GPU (Santa Clara, CA, USA). The training environment was set up with Python 3.7, the Adam optimizer from PyTorch, and a Windows 10 operating system. The hardware configuration included an NVIDIA GeForce RTX 3090 GPU, 16 GB of RAM, and an Intel Core i7 CPU, with a batch size of 1. The initial learning rate was set and halved every 20 epochs, with each experiment running for a total of 120 epochs. All comparison experiments used the same dataset and noise. However, the results from the retrained comparison experiments differ from those reported in the original paper, due to differences in the dataset size and blur kernel sizes used during training. The proposed model was compared with the state-of-the-art unfolding algorithm [22] and various deep learning-based blind deblurring models [6; 7; 24; 25] across several benchmark datasets. The goal of this comparison was to evaluate not only the overall performance of the proposed model but also its behavior under different degradation conditions. Five evaluation metrics were used: peak signal-to-noise ratio (PSNR) [26], SSIM [27], multi-scale structural similarity index measure (MS-SSIM) [28], learned perceptual image patch similarity (LPIPS) [29], and computational time. These metrics were selected to capture both traditional pixel-wise accuracy (PSNR, SSIM, MS-SSIM) and perceptual quality (LPIPS), while also considering computational efficiency (time). The performance across different datasets was analyzed to highlight how the model's effectiveness varies with factors such as image complexity, noise levels, and dataset characteristics. Additionally, the model's parameters, size, and floating point operations per second (FLOPs) [30] were evaluated in the first experiment to assess its computational efficiency. While the model demonstrated strong performance in terms of both accuracy and perceptual quality, we also carefully examined its computational cost. The results indicate that the model performs competitively in terms of FLOPs compared to other deep learning-based deblurring models, with a favorable trade-off between accuracy and efficiency. The table \(\dagger/\!\! metrics, highlighting its superior ability to handle this type of image degradation. Notably, the DUBLID architecture is 0.17 M in parameters and 727 KB smaller than the proposed model, suggesting a simpler architecture that may not capture image restoration complexities as effectively. Additionally, assessing model effectiveness should consider factors like computational efficiency, training convergence speed, and robustness to noise for a comprehensive evaluation. To demonstrate the efficiency of our model, we present a comparison of model parameters and processing times for single images in Table 1. Our model shows a clear advantage in data requirements, as it performs well on smaller datasets. In experiments where 400 training images were split into 1200 samples, our method outperformed others, reducing the need for large amounts of labeled data and minimizing preparation time and cost. Additionally, we optimize the number of parameters and model complexity, which reduces computational demands compared to models with millions of parameters. Finally, our simplified architecture provides better performance than both traditional and deep learning-based deblurring methods, completing tasks faster while maintaining quality. Figure 3 provides a visual representation of the image restoration effects resulting from linear motion blur across the various models tested. Upon careful comparison, the proposed model demonstrates clear advantages in terms of deblurring effectiveness. This is particularly evident in the resulting images, which appear visually clearer and more natural compared to those restored by alternative models.