Traditional and Soft Computing Techniques for Image Enhancement

Abstract

Now-a-days, there is a growing demand for image processing. The target of image enhancement is to find details present in images having low luminance for better image quality. Enhancement is required to improve the picture quality. In this process, we can enhance an image, by applying the suitable technique. In enhancement, there is a conversion in image contrast, quality, color vision, brightness, clarity etc. So we need image enhancement. A comparative survey is carried out in this paper, explaining traditional and soft computing techniques. This paper clarifies a study of traditional such as edge detection of an image and fuzzy logic based soft computing for improvement of an image. In the result section output of image is shown as edges using traditional as well as fuzzy. A small description is also study for picture improvement using different soft computing and optimization techniques such as Neural network, Convolution Neural Network, Ant Bee Colony, Particle Swarm Optimization etc. in literature survey and in comparative table. It is concluded that Image enhancement can be done by traditional method, soft computing and optimization method. Image enhancement has found various vision applications that have the ability to enhance the visibility of images. To enhance an image it is very important that image should be clear, so before using the enhancement techniques we should need to learn about the enhancement. So this paper described a survey of image enhancement with different techniques. In future scope of this paper we can find out different type of parameters like PSNR, MSE and execution time, also we can use optimization technique. We are also showing a comparison table of image enhancement based on traditional, soft computing and optimization techniques with its open scope.