# Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks
## 4 Experimental Results
### Image Denoising Experimental Results  
To comprehensively evaluate the denoising performance of the proposed QIS-SPFT, both quantitative and qualitative metrics were applied. For quantitative evaluation, the proposed QIS-SPFT was compared with three existing image reconstruction and denoising methods for QIS: the MLE method [15], the TD-BM3D method [17], QISNet [18] and DPIR [31]. PSNR was adopted to quantitatively assess the performance of QIS-SPFT. For grayscale synthetic QIS noisy images, the Set12 [29] and BSD68 [29] datasets were selected for testing, and four noise levels were set: Noise level 1 (\(T=16\), \(K=4\), \(\sigma_{\text{read}}=0.2\)e-), Noise level 2 (\(T=16\), \(K=4\), \(\sigma_{\text{read}}=0.4\)e-), Noise level 3 (\(T=16\), \(K=2\), \(\sigma_{\text{read}}=0.2\)e-), Noise level 4 (\(T=16\), \(K=1\), \(\sigma_{\text{read}}=0.4\)e-). The average PSNR results on the BSD68 dataset [29] are shown in Table 2.  
The PSNR evaluation results for different noise levels on the Set12 dataset [29] are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Noise Level 1** & **Noise Level 2** & **Noise Level 3** & **Noise Level 4** \\ \hline MLE [15] & 20.32 & 19.98 & 19.65 & 13.84 \\ TD-BM3D [17] & 28.89 & 28.76 & 27.86 & 23.66 \\ QISNet [18] & 29.31 & 29.01 & 28.32 & 24.32 \\ DPIR [31] & 30.01 & 29.33 & 28.79 & 25.42 \\ QIS-SPFT (proposed) & 30.32 & 29.78 & 29.13 & 25.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average PSNR evaluation results under four different noise levels in the BSD68 dataset [29]. To further illustrate the adaptability of the reconstruction method proposed in this paper across various scenarios, noisy images (\(T=16\), \(K=3\), \(\sigma_{\text{read}}=0.2\)e-) were processed using three different datasets: Set12 [29], BSD68 [29] and Kodak24 [30]. The PSNR results were averaged for each dataset and the experimental result indicated that the proposed method outperformed the comparative methods. The experimental result is shown in Table 4.  
Quantitative analysis results demonstrate that the proposed QIS-SPFT method exhibits significant performance advantages under various noise levels on the Set12 [29], BSD68 [29] and Kodak24 [30] datasets. Compared to existing methods MLE [15], TD-BM3D [17], QIS-Net [18] and DPIR [31], QIS-SPFT achieves higher PSNR values, proving its superiority in QIS image denoising tasks. The proposed method demonstrates excellent denoising results in both low-noise and high-noise scenarios, showing strong adaptability and robustness. For the qualitative evaluation of the denoising effectiveness of the proposed QIS-SPFT method, visual comparisons of reconstructed images were conducted. Specifically, regions of interest were selected to highlight key differences among different methods, as illustrated in Figures 6-10. These figures provide a clear visual representation of how the proposed QIS-SPFT method enhances image clarity and effectively suppresses noise. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **MLE [15]** & **TD-BM3D [17]** & **QISNet [18]** & **DPIR [31]** & **QIS-SPFT** \\ \hline
**Set12** [29] & 20.33 & 28.71 & 29.26 & 29.87 & 30.12 \\
**BSD68** [29] & 20.12 & 27.97 & 28.48 & 29.44 & 29.76 \\
**Kodak24** [30] & 20.85 & 29.24 & 30.12 & 30.68 & 31.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average PSNR evaluation results under three different testing datasets.