## 2 Related Work  
Various image enhancement methods have been implemented to improve image detail, especially the quality of medical images such as retinal fundus images, MRI brain images, and other types of medical images [5; 29]. The more traditional image enhancement methods such as histogram equalization [30] were used primarily for medical images, and they worked very well and improved medical images with their excellent performance. Histogram equalization is divided into two types, global histogram equalization (GHE) and local histogram equalization (LHE). These techniques perform well on normal medical images, but they do not perform well on abnormal cases of medical images due to low-varying contrast, noise, and many abnormalities. There are many other contrast enhancement methods such as fuzzy contrast enhancement methods and local contrast normalization methods [31; 32]. All these methods depend on the spatial filter, which provided the high frequency components in the original images, which can lose the details of the images. These methods are mainly based on the concepts of locally normalized pixels of medical images with zero mean and unit variance to maintain varying and low contrast as well as improving the overall image contrast. Due to the high frequency (high pass filters), these noise techniques resulted in enhanced images. Contrast adjusted filters were introduced by researchers [33; 34; 35], but these filters are not successful in medical images; these filters only work in small-sized images and take more time to compute. Later, wavelet transform was introduced for medical images for denoising, as well as to improve contrast. The main parameters of wavelet transform are the selection of appropriate wavelet transform parameters based on the properties of the image. A fast discrete wavelet transform method was proposed by [PERSON] [36], and this method is used for many applications. Algorithms [37; 38] were proposed to improve mammographic images based on wavelet transform, and [PERSON] et al. [39; 40] implemented the method to improve mammographic images based on the equalization of a wavelet-based histogram. Almost all wavelet transform methods are based on the concepts of multiscale analysis, which decomposes the image into high and low frequency components of different resolutions. For proper enhancement, the details of the image enhancement with wavelet coefficients are required using multiple values based on the adaptive gain, and an en -hanced image is obtained by reconstructing the image from the d wavelets. However, it is difficult to obtain an optimal coefficient depending on the nature of the medical images. There is a need for a technique that works on iterative methods to provide a well contrasted image. After analyzing the limitations of all these enhancement methods, many researchers have used histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE), and brightness preserving bi-histogram equalization (BBHE). These techniques worked well on a few images, but they obtained over-enhanced images on many medical images, which made it difficult to analyze the details of the images. We have selected these traditional techniques as a comparative analysis for our proposed enhancement technique. [PERSON] et al. [41] proposed a retinex-based method for contrast enhancement of natural images. They used multiscale retinex to remove the impact of lighting on the image and obtained a nicely detailed image, and they adjusted the lighting using a gamma correction with a weighted distribution. [PERSON] et al. [42] implemented a fast retinex-based algorithm (RBFA) to achieve a low-illuminated image that could restore the low-illuminance image (reflectance image). Their method was also based on the adaptive gamma correction to obtain a detailed image, and they also used the natural images to obtain a better enhanced image. [PERSON] et al.