# Novel Deep Level Image State Ensemble Enhancement Method for M87 Imaging
## 1 Introduction  
This paper investigates a new method to enhance features and improve color image processing. The Image State Ensemble Enhancement (ISEE) and Image State Ensemble Decomposition (ISED) methods can denoise and enhance contrast. Although the topic we have chosen to analyze is astronomical, these methods can easily be applied to color medical imaging. Denoising and contrast enhancement are commonly applied to medical imaging and have been the subject of intense research [1]. ISED and ISEE reconstructions can denoise, enhance the contrast, and provide feature extraction. ISEE improves the full-reference metrics and narrows the bandwidth of the filter, which allows for the extraction of finer details than ISED. See our unpublished histological image examples of ISED and ISEE reconstructions available online at [2] for more details. The proposed deep level ISED and ISEE reconstructions differ from traditional types of filters. ISED and ISEE rely on some basic precepts of quantum mechanics to statistically determine and generate filtered color images based on possible image outcomes [3]. ISED and ISEE reconstructions are performed in the spatial domain. Common spatial domain filters often rely on convolution kernels or a window to modify a group of pixels [4]. Mean filtering and median filtering are common methods to smooth the image to reduce types of noise, but at the cost of adding some blurring to the image [5]. ISED and ISEE use a pixel-wise cross-channel relationship that does not suffer from blurring issues. However, the drawback to ISEE and ISED is that you must have at least two different channels to formulate the \(\psi\)-ensemble relationship. This method is designed for color images only. Various types of equalizations are also performed commonly on images. Histogram equalization redistributes the intensities of the pixels for close contrast valued images. At times, this can be a godsend, and at other times, you will end up with unrealistic oversaturated images [2]. With ISED or ISEE, oversaturation can never happen. The generated image never exceeds the intensity values of the original image, and the pixels intensities are not redistributed thought the image. ISED and ISEE reconstructions are an entirely new class of digital filters that have the ability to selectively remove small specific regions of information from the image, and the ISEE reconstruction restores part of the filtered information and generates possible narrowband filtered images. ISED and ISEE have similar characteristics to optical filters. Our previous study used ISED to reduce unwanted effects of glare, noise reduction, and image artifacts [1]. The perceptual quality of the image was improved and made evident by lower Perception-based Image Quality Evaluator (PIQE), Natural Image Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) for most ISED images. While no-reference quality metrics were improved, the full-reference quality metrics structural similarity (SSIM) index, mean-squared error (MSE), and the peak signal-to-noise ratio (PSNR) decreased--a behavior that was anticipated when performing image decomposition [1]. This paper addresses the issue of decreased performance in SSIM, MSE, and PSNR and introduces ISEE deeper decomposition levels (Ln), while presenting an image reconstruction process algorithm deemed ISEE. In the previous paper \"Novel Image State Ensemble Decomposition Method for M87 Imaging\", we were able to generate ISED images that had improved no-reference image quality metrics over those of the original images provided by National Aeronautics and Space Administration (NASA). For certain generated images, there was a reduction in the noise, glare, and the filter that made it possible to observe previously unseen structures [1]. The galactic core had greater detail, and the infrared (IR) outer ring had clearer boundaries [1].