## 2 Literature Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Data Type** & **Techniques** & **Descriptions** & **Performance Metrics** & **Results** \\ \hline
[15] & CT images & \begin{tabular}{c} Fuzzy employed \\ dynamic histogram \\ equalization \\ \end{tabular} & \
\begin{tabular}{c} Contrast enhancement is considered a \\ significant aspect of medical analysis \\ because diagnosis error can be \\ minimized only by utilizing a \\ better-quality image. \\ \end{tabular} &
\begin{tabular}{c} Average Pixel \\ Intensity, Standard \\ Deviation, \\ Average gradient, \\ Entropy, \\ Mutual Information \\ \end{tabular} \\ \hline
[16] & MRI images & Contrast stretching & \
\begin{tabular}{c} An effective data hiding technique that \\ enhances the visual quality of \\ watermarked images while ensuring \\ efficient concealment of the \\ embedded data. \\ \end{tabular} & PSNR, SSIM &
\begin{tabular}{c} 16.24 \\ 0.9932 \\ \end{tabular} \\ \hline
[17] & Digital images & \begin{tabular}{c} Dynamic Histogram \\ Equalization \\ \end{tabular} & \begin{tabular}{c} Enhancing the quality of MRI-generated \\ images is crucial, as the complexity of \\ these images often poses challenges for \\ specialists during their analysis \\ and interpretation. \\ \end{tabular} & \
\begin{tabular}{c} PSNR, SSIM, \\ MSE \\ \end{tabular} &
\begin{tabular}{c} 32.00 \\ 0.9000 \\ 0.300 \\ \end{tabular} \\ \hline
[18] & X-ray Image & \begin{tabular}{c} Modified shark smell \\ optimization \\ \end{tabular} & \begin{tabular}{c} The main idea here is to introduce an \\ optimization problem by considering \\ both global and local enhancement to \\ achieve a strong image \\ enhancement method \\ \end{tabular} & \
\begin{tabular}{c} Contrast, CNR, EME, \\ WPSNR, \\ Homogeneity \\ \end{tabular} &
\begin{tabular}{c} 0.89, 78.12 \\ 32.41 17.13, \\ 0.85 \\ \end{tabular} \\ \hline
[19] & X-ray image & \
\begin{tabular}{c} Fuzzy gray-level \\ difference histogram \\ equalization \\ \end{tabular} &
\begin{tabular}{c} The gray-level difference of an input \\ image is calculated using the binary \\ similar patterns. \\ \end{tabular} & Entropy, PSNR & 7.01, 38.15 \\ \hline
[20] & MRI images & \begin{tabular}{c} Piecewise linear \\ histogram \\ equalization \\ \end{tabular} & \begin{tabular}{c} The primary function involves ensuring a \\ uniform distribution of histogram \\ components across the entire \\ grayscale range \\ \end{tabular} & \
\begin{tabular}{c} Entropy, edge \\ enhancement \\ index (EHI) \\ \end{tabular} &
\begin{tabular}{c} 5.2001 \\ 5040 \\ \end{tabular} \\ \hline
[23] & MRI image & \
\begin{tabular}{c} Limited Dynamic \\ Weighted Histogram \\ Equalization \\ \end{tabular} &
\begin{tabular}{c} The proposed reversible data \\ hiding-based limited dynamic weighted \\ histogram equalization technique for \\ abnormal tumor regions improves the \\ contrast and transmits hidden \\ secret information. \\ \end{tabular} & PSNR & 34.65 \\ \hline
[21] & \begin{tabular}{c} Digital \\ images \\ \end{tabular} & \begin{tabular}{c} Adaptive Histogram \\ Equalization \\ \end{tabular} & \begin{tabular}{c} The algorithm’s efficacy extends beyond \\ enhancing medical images; it also caters \\ to the enhancement of ordinary images \\ captured under both low-light and \\ daylight conditions. \\ \end{tabular} & \
\begin{tabular}{c} PSNR, \\ NAE \\ \end{tabular} &
\begin{tabular}{c} 53.4047 \\ 1.0267 \\ 1.0267 \\ \end{tabular} \\ \hline
[24] & MRI images & \begin{tabular}{c} Histogram \\ equalization, \\ adaptive gamma \\ correction \\ \end{tabular} & \begin{tabular}{c} Extracting pertinent information from \\ low-contrast and poor-quality MRI \\ images proves to be a formidable task. \\ \end{tabular} & \
\begin{tabular}{c} PSNR, \\ MSE \\ \end{tabular} &
\begin{tabular}{c} 29.07 \\ 80.92 \\ \end{tabular} \\ \hline
[22] & \begin{tabular}{c} CT and MRI \\ images \\ \end{tabular} & \
\begin{tabular}{c} Chaos-based \\ optimization \\ \end{tabular} &
\begin{tabular}{c} Medical images often exhibit \\ characteristics such as low contrast, \\ significant noise, and compact \\ dimensions. The accurate identification \\ of anomalies within these images heavily \\ relies on their quality and level of clarity. \\ \end{tabular} & Entropy, & 6.65 \\ \hline \hline \end{tabular}
\end{table}