Image enhancement is a technique that increases the visual contrast in a designated intensity range. Contrast is an act of distinguishing by comparing differences. Morphological transformation and block analysis are used to detect the background of various social and medical images. Opening by reconstruction method of contrast image transformation can be defined by two operators-opening and closing. The first operator makes use of the information from block analysis, while the second transformation utilizes the opening by reconstruction. The Later is used to define the multi background notion. The complete image processing is being implemented using JAVA simulation model. Quality of image enhancement is assessed by different techniques. In this paper, High performance Computational techniques involving contrast enhancement and noise filtering on various medical , social images are developed using Weber's law. Image quality assessment is compared by different techniques. The values of all the quality assessment parameters are found to be in the standard expected ranges thereby confirming the enhancement of quality of images.
The bioelectric potentials associated with muscle activity constitute the electromyogram (EMG). These EMG signals are low-frequency and lower-magnitude signals. In this paper, it is presented that Jordan/Elman neural network can be effectively used for EMG signal noise removal, which is a typical nonlinear multivariable regression problem, as compared with other types of neural networks. Different neural network (NN) models with varying parameters were considered for the design of adaptive neural-network-based filter which is a typical SISO system. The performance parameters, that is, MSE, correlation coefficient, N/P, and t, are found to be in the expected range of values.
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