A Survey on Image Enhancement Techniques: Classical Spatial Filter, Neural Network, Cellular Neural Network, and Fuzzy Filter

Present day applications require various kinds of images and pictures as sources of information for interpretation and analysis. Whenever an image is converted from one form to another, such as, digitizing, scanning, transmitting, storing, etc., some form of degradation occurs at the output. Hence, the output image has to undergo a process called image enhancement which consists of a collection of techniques that seek to improve the visual appearance of an image. In this paper, a classical spatial filter, neural network (NN), cellular neural network (CNN) and fuzzy filters are presented for the noise reduction of images that are corrupted with additive noise. A three layer neural network is trained for few test images and is used to filter the corrupted colour images. A single layer CNN is developed to reduce the noise in the colour image and compared with that of the classical spatial filter. A new fuzzy filter technique is studied with respect to noisy gray scale images. All the techniques produce convincing results when applied to additive (Gaussian) noisy images. Experimental results are obtained based on the mathematical models of expert systems and compared by numerical measures and visual inspection. It is envisaged to train CNN using gradient descent back-propagation algorithm for better results and extend fuzzy filter technique to reduce noise in colour images.