Fuzzified Contrast Enhancement and Segmentation For Nearly Invisible Images

Any computer vision application must first improve a picture before continuing to process it color details losses during the enhancement process is a prevalent issue with most current techniques when applied to photographs that are essentially unnoticeable the qualitatively undetectable image should be improved while maintaining its freshness and coloring. Histogram equalization, a traditional approach of contrast enhancement, resulting in more than enhancement of something like the picture, particularly one with poorer resolution. The objective of this research is to develop an innovative fuzzy inference system capable of enhancing the contrast of low-resolution photos while simultaneously addressing any existing limitations, existing techniques and segmenting the tumor in MRI images. The outcomes from the two methods are contrasted. Throughout this research, the technique results in a very tiny change in intensity value while maintaining the image's information about color and brightness. The method enhances striking contrast while preserving naturalness without introducing any artefacts. Active contour processing on these photos produces extremely accurate segmentation results. Mainly this is used to detect the tumor in MRI images with some basic morphological operations.