## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **SSIM**\(\uparrow\) & **PSNR**\(\uparrow\) & **\#Param** & **Training Time** \\ \hline LDCT & 0.6234 \(\pm\) 0.0035 & 30.14 \(\pm\) 0.05 & — & — \\ \hline PViT & 0.8609 \(\pm\) 0.0012 & 41.21 \(\pm\) 0.08 & 1.68 M & 15 min \\ GAM-PViT (DVH) & 0.8421 \(\pm\) 0.0025 & 38.23 \(\pm\) 0.08 & 1.66 M & 15 min \\ LAM-PViT & 0.8613 \(\pm\) 0.0018 & 40.66 \(\pm\) 0.06 & 1.66 M & 15 min \\ \hline GLAM-PViT (MSE) & 0.8428 \(\pm\) 0.0022 & 39.05 \(\pm\) 0.07 & 1.70 M & 18 min \\ GLAM-PViT (SSIM) & 0.8452 \(\pm\) 0.0019 & 39.16 \(\pm\) 0.10 & 1.71 M & 18 min \\ GLAM-PViT (PL) & 0.8645 \(\pm\) 0.0010 & 40.28 \(\pm\) 0.05 & 1.75 M & 20 min \\ \hline GLAM-PViT (proposed) & 0.8742 \(\pm\) 0.0009 & 43.25 \(\pm\) 0.03 & 1.82 M & 30 min \\ \hline Benchmark 1: RED-CNN & 0.8413 \(\pm\) 0.0030 & 36.92 \(\pm\) 0.09 & 3.33 M & 50 min \\ Benchmark 2: TED-Net & 0.8603 \(\pm\) 0.0016 & 41.03 \(\pm\) 0.07 & 3.28 M & 80 min \\ Benchmark 3: DSC-GAN & 0.8524 \(\pm\) 0.0025 & 40.35 \(\pm\) 0.08 & 4.16 M & 120 min \\ Benchmark 4: BM3D * & 0.8152 \(\pm\) 0.0040 & 34.27 \(\pm\) 0.10 & n/a & 60 min \\ Benchmark 5: DRLEMP & 0.8579 \(\pm\) 0.0020 & 37.64 \(\pm\) 0.06 & 2.80 M & 60 min \\ \hline \hline \end{tabular} \(\dagger\) Higher SSIM/PSNR values indicates more accurate denoised image. * BM3D involves algorithmic parameters and settings (e.g., block size, search window size, thresholding parameters) that can be tuned, but these are not learned from data in the same manner as neural network weights.  
\end{table} 
Table 5: **Abdomen dataset**. Average SSIM and PSNR with No. of parameters using two transformer blocks for the PViT model variants (GAM: gradient attention module, LAM: Laplacian attention module, GLAM: gradient–Laplacian attention module) and variants of the loss functions (MSE, SSIM, PL, proposed: MSE + SSIM + PL) used for the training (100 epochs). Uncertainties were calculated using standard error of the mean (SEM). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **SSIM**\(\uparrow\) & **PSNR**\(\uparrow\) & **\#Param** & **Training Time** \\ \hline LDCT & 0.3489 \(\pm\) 0.0040 & 26.32 \(\pm\) 0.08 & — & — \\ \hline PViT & 0.7932 \(\pm\) 0.0015 & 36.19 \(\pm\) 0.05 & 1.68 M & 15 min \\ GAM-PViT (DVH) & 0.8064 \(\pm\) 0.0025 & 34.72 \(\pm\) 0.06 & 1.66 M & 15 min \\ LAM-PViT & 0.8107 \(\pm\) 0.0010 & 35.28 \(\pm\) 0.05 & 1.66 M & 15 min \\ \hline GLAM-PViT (MSE) & 0.8012 \(\pm\) 0.0035 & 34.17 \(\pm\) 0.09 & 1.70 M & 18 min \\ GLAM-PViT (SSIM) & 0.8056 \(\pm\) 0.0028 & 35.19 \(\pm\) 0.03 & 1.71 M & 18 min \\ GLAM-PViT (PL) & 0.8124 \(\pm\) 0.0018 & 35.82 \(\pm\) 0.04 & 1.75 M & 20 min \\ \hline GLAM-PViT (proposed) & 0.8224 \(\pm\) 0.0010 & 36.71 \(\pm\) 0.02 & 1.82 M & 30 min \\ \hline Benchmark 1: RED-CNN & 0.6423 \(\pm\) 0.0035 & 29.63 \(\pm\) 0.10 & 3.33 M & 50 min \\ Benchmark 2: TED-Net & 0.7725 \(\pm\) 0.0020 & 34.92 \(\pm\) 0.07 & 3.28 M & 80 min \\ Benchmark 3: DSC-GAN & 0.6234 \(\pm\) 0.0045 & 33.25 \(\pm\) 0.06 & 4.16 M & 120 min \\ Benchmark 4: BM3D * & 0.5673 \(\pm\) 0.0050 & 27.31 \(\pm\) 0.09 & n/a & 60 min \\ Benchmark 5: DRLEMP & 0.6636 \(\pm\) 0.0030 & 31.33 \(\pm\) 0.05 & 2.80 M & 60 min \\ \hline \hline \end{tabular} \(\dagger\) Higher SSIM/PSNR values indicates more accurate denoised image. * BM3D involves algorithmic parameters and settings (e.g., block size, search window size, thresholding parameters) that can be tuned, but these are not learned from data in the same manner as neural network weights.  
\end{table} 
Table 6: **Chest dataset**. Average SSIM and PSNR with No. of parameters using two transformer blocks for the PViT model variants (GAM: gradient attention module, LAM: Laplacian attention module, GLAM: gradient–Laplacian attention module), and variants of the loss functions (MSE, SSIM, PL, proposed: MSE + SSIM + PL) used for the training (100 epochs). Uncertainties were calculated using standard error of the mean (SEM).