# Novel Deep Level Image State Ensemble Enhancement Method for M87 Imaging
## 1 Introduction
The images produced from that study were the result of a new class digital narrowband filters that can be applied to extract features in color images. In our case, the feature that we want to extract is glare and noise. As it turned out, there was a great deal of blue-biased noise in the original image. The ISED method was successful in reducing this noise, and the quantum-based equations proved to be fully functional and useful. However, this initial method has a cost. The full-reference quality metrics were lowered. The filtered portions of noise, glare, and image artefacts are the desired effect for specific ISED states. Our new method, ISEE, takes the ISED method much further by providing deeper levels of image decomposition, where the variable \(n\) in Ln denotes the number of iterations the test image goes through in the ISED process in relation to deeper Ln. This further decomposes the image; however, more information is contained within the filter as decomposition increases. We will show this is an advantageous byproduct of the ISED process. For this study, the test image goes through six levels of decomposition (L6) and ISEE reconstruction in order to uncover never-before-seen details in the vicinity of an active galactic nuclei (AGN) that is ejected out of Virgo A's (M87) core. The results of this paper are proof of concept and a practical application of imaging using deep Ln ISED and ISEE reconstruction. All processed test images for the deep Lns and ISEE reconstructions are available online [2]. ISEE images take a specific decomposed ISED image and combine it with the filtered image information. As the levels of decomposition increase, the information in the ISED filter plus image combine to produce a narrower band digital filter. By design, this produces higher SSIM, MSE, and PNSR. We are looking for the \"sweet spot\" in the image. Our main goal is to determine if we can see where the black hole is hiding and what else is hiding along with it. We are looking for structures in these galactic images that are not apparent in the original postprocessed image, but based on the previous study, we knew there was more information contained within the original image and developed ISEE as a way to extract new features while improving the full-reference quality metrics. This is applicable if the appropriate filter is selected for the ISEE reconstruction; the image evolves to become more like the original test image. The equivalent state ISED image combined with the equivalent state ISED filter produces the original image. The same is also true for deeper Ln ISED filters and images. However, with deeper Ln ISED, the information in the decomposed image decreases, and the image information contained in the filter increases. Therefore, as a result, deeper Ln for ISED images decreases in SSIM. The ISEE reconstruction takes advantage of this transfer of information to the \"filter side\" of the image. For ISEE generated images, the SSIM increases in general for every additional Ln. When examining the histograms and the cumulative distribution functions (CDFs) of the ISEE images, one can clearly see that some ISEE images converge to the original image if given enough Ln and other ISEE images remain divergent. Consequently, ISED images from the previous study that had high SSIM produce the divergent case for ISEE images. The divergent case is, in part, caused by the limited amount of image information contained in the ISED filter. The National Aeronautics and Space Administration's (NASA's) infrared Spitzer Space Telescope (SST) has long peered into the black abysses of our universe. SST was launched in 2003 to observe the coldest brown dwarf stars, the furthest red-shift galaxies, and uses its infrared imaging to see through the interstellar dust to discover what is hidden beneath it [6]. SST's Infrared Array Camera (IRAC) is well suited to find AGNs, ultraluminous galaxies, superplanets, and protoplanetary discs [7].