An image with high contrast and brightness is called a fine quality image while a poor quality image is identified by low contrast and poorly defined boundaries between the edges. Image enhancement can be considered as transforming poor quality image into good quality image to make its meaning more clear for human perception or analysis of the machine. Implement image improvement with fuzzy techniques and improve the image. This article presents an investigation to improve the quality of the image by improving the detailed details of the image degraded using diffuse techniques.
The enhancement of the image alters an image to make its meaning more clear to human observers. It is often used to increase the contrast in images that are substantially dark or light. Improving noisy image data is a very difficult topic in research and application fields. One of the most used algorithms for image enhancement is the equalization of the global histogram which adjusts the intensity histogram to approximate a uniform distribution. The main disadvantage of global histogram equalization is that the overall properties of the image may not be applied properly in a local context. In fact, the modification of global histograms treats all regions of the image equally and therefore often produces poor local performance in terms of preservation of details. Histogram global stretching and equalization techniques do not always produce good results, especially for images with large spatial contrast variation.