Abstract

Image Enhancement (IE) is vital and one of the significant parts of Image processing. It is necessary to enhance the contrast and reduce the noise to improve the quality of the image.IE is one of the processes for altering the visibility of an image. It is widely applied in different fields such as medical science, industry, military, agriculture, etc. Image Enhancement is modifying digital images to make the image appear pleasing to the viewer. Several research works have been done on Image Enhancement. This paper also discusses the Self-Calibrated Illumination method (SCIM) and the HE method. SCIM along with HE Image Enhancement techniques and algorithms can improve the visual quality of the digital image. There are several techniques to enhance the quality of an image to extract meaningful information from the image. The wellknown techniques for image enhancement are the RetiNex-based method, Weber-Fechner method, Linear Regression algorithm-based method, Histogram Equalization, Fourier transform, etc. This paper reviews and compares the research work carried out in image enhancement based on the Histogram Equalization Method by different authors. This paper also reviews the merits and drawbacks along with the enhanced images and histogram plots obtained in these methods developed over the past decades. Finally, it concludes what further work needs to be done for a better quality of image