# Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks
## 4 Experimental Results
### Image Denoising Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & **Cameraman** & **House** & **Pepper** & **Starfish** & **Monarch** & **Parrot** \\ \hline Noise level 1 & & & & & & \\ MLE [15] & 19.72 & 22.11 & 20.92 & 19.76 & 19.74 & 18.88 \\ TD-BM3D [17] & 29.21 & 33.32 & 31.44 & 30.31 & 29.34 & 29.04 \\ QISNet [18] & 29.74 & 33.77 & 31.91 & 30.89 & 29.83 & 29.64 \\ DPIR [31] & 30.43 & 34.42 & 32.63 & 31.52 & 30.62 & 30.31 \\ QIS-SPFT (proposed) & 30.77 & 34.75 & 32.80 & 31.95 & 30.91 & 30.75 \\ \hline Noise level 2 & & & & & & \\ MLE [15] & 18.21 & 21.42 & 18.91 & 18.43 & 19.22 & 17.84 \\ TD-BM3D [17] & 28.77 & 31.64 & 30.21 & 29.02 & 29.88 & 28.44 \\ QISNet [18] & 29.33 & 32.12 & 30.84 & 29.54 & 30.34 & 29.01 \\ DPIR [31] & 29.72 & 33.53 & 31.23 & 30.30 & 30.77 & 29.33 \\ QIS-SPFT (proposed) & 30.07 & 33.92 & 31.71 & 30.65 & 31.02 & 29.84 \\ \hline Noise level 3 & & & & & & \\ MLE [15] & 18.19 & 21.34 & 18.94 & 18.88 & 18.26 & 18.66 \\ TD-BM3D [17] & 27.22 & 31.24 & 28.34 & 27.21 & 27.84 & 27.34 \\ QISNet [18] & 28.41 & 31.86 & 28.91 & 27.86 & 28.52 & 27.88 \\ DPIR [31] & 29.66 & 32.91 & 30.64 & 29.15 & 29.72 & 28.81 \\ QIS-SPFT (proposed) & 29.90 & 33.24 & 30.89 & 29.46 & 30.13 & 29.16 \\ \hline Noise level 4 & & & & & & \\ MLE [15] & 13.23 & 15.44 & 14.10 & 12.98 & 13.32 & 13.58 \\ TD-BM3D [17] & 23.91 & 25.71 & 23.32 & 23.11 & 23.97 & 24.23 \\ QISNet [18] & 24.32 & 26.15 & 23.91 & 23.74 & 24.51 & 24.72 \\ DPIR [31] & 25.88 & 27.02 & 26.41 & 24.97 & 26.24 & 26.58 \\ QIS-SPFT (proposed) & 26.10 & 28.34 & 26.85 & 25.35 & 26.62 & 26.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PSNR evaluation results under four different noise levels in the Set12 dataset [29]. Figure 8: Comparison of denoising results of four methods for “boat” in Set12 dataset: (**a**) Ground Truth, (**b**) MLE [15], (**c**) TD-BM3D [17], (**d**) QISNet [18], (**e**) DPIR [31], (**f**) Proposed. Figure 6: Comparison of denoising results of four methods for “castle” in BSD68 dataset: (**a**) Ground Truth, (**b**) MLE [15], (**c**) TD-BM3D [17], (**d**) QISNet [18], (**e**) DPIR [31], (**f**) Proposed. Figure 7: Comparison of denoising results of four methods for “mushroom” in BSD68 dataset: (**a**) Ground Truth, (**b**) MLE [15], (**c**) TD-BM3D [17], (**d**) QISNet [18], (**e**) DPIR [31], (**f**) Proposed. Compared with existing denoising techniques such as MLE [15], TD-BM3D [17], QIS-Net [18] and DPIR [31], QIS-SPFT demonstrates superior performance by preserving finer image details and improving the visibility of crucial regions. The results indicate that QIS-SPFT effectively reduces non-ideal noise while preserving the structural integrity and texture of the original images, which is essential for high-quality QIS image reconstruction. These findings further confirm that QIS-SPFT outperforms conventional denoising  
Figure 10: Comparison of denoising results of four methods for “airplane” in Kodak24 dataset: (**a**) Ground Truth, (**b**) MLE [15], (**c**) TD-BM3D [17], (**d**) QISNet [18], (**e**) DPIR [31], (**f**) Proposed. Figure 9: Comparison of denoising results of four methods for “house” in Kodak24 dataset: (**a**) Ground Truth, (**b**) MLE [15], (**c**) TD-BM3D [17], (**d**) QISNet [18], (**e**) DPIR [31], (**f**) Proposed. approaches and establishes itself as a robust method for enhancing imaging quality in QIS image reconstruction.