## 4 Results and Discussion
### De-Blurring Image
In particular, the rate of decrease in the correlation coefficient became more pronounced once the depth exceeded 10.1 mm. Similarly, the Attention Res-UNet model yielded test results indicating a high correlation coefficient ranging from 0.9308 to 0.9069 for depths between 0.1 and 5.0 mm. Beyond 5.0 mm, the correlation coefficient gradually decreased, reaching 0.8845 at a depth of 14.0 mm. In particular, for depths greater than 10.0 mm, the rate of decrease in the correlation coefficient increased. Finally, for depths ranging from 14.1 to 20.0 mm, the correlation coefficient exhibited a rapid decline from 0.8942 to 0.8876 at a depth of 20.0 mm. Once again, the rate of decrease in the correlation coefficient decreased rapidly for depths exceeding 14.1 mm. In the subsequent experiment, the size of the training input image was modified from \(256\times 256\) pixels to \(112\times 112\) pixels while keeping the other training parameters in Table 2 unchanged. The results obtained from this adjustment are depicted in Figure 12.  
The test results of the Attention U-Net model revealed that within the depth range of 0.1 to 5.0 mm, the correlation coefficient initially reached a high value and gradually decreased from 0.9186 to 0.8722. The highest correlation coefficient was achieved at a depth of 0.1 mm, registering a value of 0.9186. As the depth increased from 5.1 to 10.0 mm, the correlation coefficient experienced a gradual decrease, reaching 0.8184 at a depth of 10.0 mm. Subsequently, for depths ranging from 10.1 mm to 20.0 mm, the correlation coefficient exhibited a rapid drop from 0.8172 to 0.6927 at a depth of 20.0 mm. Remarkably, once the depth surpassed 7.0 mm, the rate of decline in the correlation coefficient with respect to depth became more pronounced. Similarly, the Attention Res-UNet model yielded noteworthy test results. At depths ranging from 0.1 to 5.0 mm, the correlation coefficient reached a high value and gradually decreased from 0.9337 to 0.9023. The highest correlation coefficient was observed at a depth of 0.1 mm, yielding a value of 0.9337. For depths extending from 5.1 to 14.0 mm, the correlation coefficient exhibited a gradual decrease from 0.9080 to 0.8736 at a depth of 14.0 mm. In particular, depths greater than 10.0 mm experienced an accelerated decline in the correlation coefficient. Finally, within the depth range of 14.1 to 20.0 mm, the correlation coefficient decreased rapidly from 0.8717 to 0.8539 at a depth of 20.0 mm. Once the depth surpassed 11.6 mm, the rate of decrease in the correlation coefficient with respect to depth decreased rapidly. Figure 12: Correlation analysis between original and deblurred images with \(112\times 112\) input image size. For the Attention U-Net model, it was observed that at depths ranging from 0.1 to 0.5 mm, employing an input size of \(112\times 112\) pixels yielded better performance, with a difference in the correlation coefficient ranging from 0.6% to 0.9%. On the contrary, at depths ranging from 0.6 to 5.0 mm, adopting an input size of \(256\times 256\) pixels achieved superior performance, exhibiting a difference in the correlation coefficient ranging from 0.02% to 3.08%. In particular, for depths ranging from 5.1 to 20.0 mm, the difference in performance between the two input sizes increased rapidly, ranging from 3.35% to 20.91%. For the Attention Res-Unet model, it was observed that at depths ranging from 0.1 to 0.9 mm, employing an input size of \(112\times 112\) pixels resulted in improved performance, with a difference in the correlation coefficient ranging from 0.4% to 0.6%. On the other hand, at depths ranging from 1.0 to 5.0 mm, adopting an input size of \(256\times 256\) pixels yielded better performance, exhibiting a difference in the correlation coefficient index ranging from 0.15% to 0.75%. Furthermore, for depths ranging from 5.1 to 20.0 mm, the difference in performance between the two input sizes increased rapidly, ranging from 0.7% to 3.55%.