# Novel Deep Level Image State Ensemble Enhancement Method for M87 Imaging
## 3 Materials and Methods
The vortex and the outburst can also be seen in the dusty core in S\({}_{6,30}\). Additionally, the counter-jet channel seems to be bent approximately 5\({}^{\circ}\) with respect to the left shockwave. This was verified with the HST/SST image overlay in Figure 2, where the SST ISEE generated image \(\tau_{6,30}\) and the HST generated ISEE image \(\tau_{6,19}\) in Figure 1b. The overlay was built in Gimp, and the top layer is the SST image with an 80% opacity. It was also rotated and cropped and saved in the PNG file format. The overlay selection of SST image \(\tau_{6,30}\) and HST image \(\tau_{6,19}\) was chosen because the SST image has the highest SSIM for \(\tau_{m,30}\) and the HST image isolated previously unseen wispy features surrounding the core. This was useful in aligning the image and revealing hidden features in the SST image. Figure 2: Tight crop of Hubble Space Telescope (HST) \(\tau_{6,19}\) and Spitzer Space Telescope (SST) \(\tau_{6,44}\) overlay, 80% opacity with drawings. The IQA metrics displayed in Table 3 for SSIM, PSNR, and MSE, (Defined in Table 4) NIQE, BRISQUE, and PIQE are well understood and are often employed as a standard in image analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\tau_{\text{m,n}}\)} & \multicolumn{6}{c}{**Image Quality Assessment Metrics ISEE**} \\ \cline{2-7}  & **SSIM** & **PSNR** & **MSE** & **NIQE** & **BRISQUE** & **PIQE** \\ \hline Original Image & 1.00 & NA & NA & 8.22 & 43.38 & 100 \\ \(\tau_{1,30}\) & 0.63 & 22.04 & 406.91 & 6.84 & 41.58 & 44.43 \\ \(\tau_{2,30}\) & 0.57 & 21.27 & 485.76 & 7.16 & 42.82 & 63.76 \\ \(\tau_{3,30}\) & 0.68 & 22.85 & 337.25 & 6.97 & 45.08 & 63.73 \\ \(\tau_{4,30}\) & 0.76 & 24.44 & 233.89 & 7.15 & 41.61 & 59.49 \\ \(\tau_{5,30}\) & 0.81 & 25.71 & 233.89 & 6.89 & 52.78 & 58.98 \\ \(\tau_{6,30}\) & 0.84 & 26.68 & 139.81 & 6.96 & 61.80 & 54.90 \\ \(\text{S}_{1,30}\) & 0.87 & 22.54 & 362.31 & 6.31 & 46.41 & 48.14 \\ \(\text{S}_{2,30}\) & 0.62 & 19.02 & 814.23 & 6.92 & 36.99 & 48.51 \\ \(\text{S}_{3,30}\) & 0.43 & 17.54 & 1146.2 & 7.25 & 45.11 & 43.21 \\ \(\text{S}_{4,30}\) & 0.31 & 16.71 & 1388.4 & 7.32 & 46.14 & 44.13 \\ \(\text{S}_{5,30}\) & 0.24 & 16.20 & 1558.3 & 7.81 & 46.45 & 47.82 \\ \(\text{S}_{6,30}\) & 0.20 & 15.89 & 1674.4 & 8.05 & 46.56 & 48.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average values for image quality assessments (IQAs) for \(\tau_{\text{m,30}}\) and \(\text{S}_{\text{m,30}}\). The SSIM produces a maximum score of one based on the images' comparative contrast, local structure, and luminescence [20]. The PSNR indicates the peak error of the image and is used to determine compression loss in images and video [22]. Higher PSNR scores are better when compared to images that have gone through a similar process. Therefore, a rough comparison can be drawn between the same state images for varied Ln. Likewise, the MSE is also a good indicator to see the behavior of ISEE for deeper Ln as a comparative trend as a predictor for Ln > 6. Lower MSE scores are better. The NIQE is trained from a database of pristine images and various images with Gaussian distortions, so it can measure arbitrary distortions in the test images; lower scores are better [23]. Additionally, BRISQUE is trained from a database of known image distortions [24; 25], while PIQE evaluates the local variance to detect block-wise distortion in order to calculate the quality of an image, where lower scores are better [26; 27]. Table 5 is for the assessment of PIQE scores, as recommended by [27]. From Table 5, it is clear as a trend, the SSIM increases apart from the L2 image. From our previous study, the ISED image seen in Figure 3a had an SSIM of 0.87. This is close to the value of the ISEE L6 image \(\tau_{6,30}\) SSIM of 0.84. The PNSR and MSE have both improved, so it is a probable indicator that the ISEE image has less noise. The NIQE is quite close, with **S1\({}_{\mathbf{1},30}\)** equal to 6.31 and \(\tau_{6,30}\) equal to 6.96.