Earticle

Neurocomputing in complex domain has yielded second generation neural networks. The neural network, which is based on complex value, contains different layers. The attributes of these layers are biases, weights, inputs and outputs. These attributes are also complex numbers. The signal processing, speech processing, learning and prediction of motion on plane are few areas in which complex domain neurocomputing is applied., since in the above said areas, the inputs and outputs are represented by complex values. It has been observed that the neural network with complex value can easily perform the transformation of geometric figures. The examples of transformations are rotation, parallel displacement of straight lines and circles. The neural network can extend to complex domain by the application of transformation. A number in complex domain is composed of different entities i.e. two real numbers and phase information. The two real numbers and phase information of any point on plane is naturally embedded in this number.

The contrast of an image is a feature which determines how image looks better visually. In this paper, we are analysing the capability of activation functions for contrast enhancement. Activation functions are classically used in neural network. In this paper, Activation function creates a mask which is operated on the image on pixel by pixel basis. On the basis of activation function the pixel value of image is changed which improves the contrast of image. We have used various activation functions such as sigmoid function, bipolar sigmoid function, RAMP function, hyperbolic tangent function. Contrast enhancement using these activation functions has been successfully applied on several dark and bright images. For performance assessment we have used Peak Signal to Noise Ratio (PSNR), absolute mean brightness error (AMBE), and Structure Similarity Index (SSIM). From experimental result, it is observed that RAMP function and hyperbolic tangent function have better image enhancement capability.