Fuzzified Contrast Enhancement and Segmentation For Nearly Invisible Images

Zaheeruddin Syed, Kanneboina Siddhartha, Thota Rahul, Aragonda Sneha, Ellandala Jhansi, K. Suganthi
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Abstract

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.
模糊对比度增强与近不可见图像分割
任何计算机视觉应用程序都必须首先改善图像,然后再继续处理它,在增强过程中,颜色细节损失是大多数当前技术普遍存在的问题,当应用于本质上不可注意的照片时,在质量上不可检测的图像应该得到改善,同时保持其新鲜度和色彩。直方图均衡化,一种传统的对比度增强方法,其结果不仅仅是图像的增强,尤其是分辨率较差的图像。本研究的目的是开发一种创新的模糊推理系统,能够增强低分辨率照片的对比度,同时解决任何现有的限制,现有的技术和分割MRI图像中的肿瘤。对比了两种方法的结果。在整个研究过程中,该技术在保持图像的颜色和亮度信息的同时,导致强度值发生非常微小的变化。该方法在不引入任何人工制品的情况下,在保持自然的同时增强了鲜明的对比度。主动轮廓处理这些照片产生非常准确的分割结果。主要是通过一些基本的形态学操作来检测MRI图像中的肿瘤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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