Improving the image resolution and contrast along with uniform brightness distribution over the entire image helps to retrieve the vital realistic information necessary for human perception and interpretation. A new procedure to improve these parameters is implemented and tested. Initially, the image is super resolute using discrete wavelet transform (DWT), stationary wavelet transform (SWT) image decomposition and bicubic interpolation. The resolute image is used for contrast enhancement by using SWT and contrast limited adaptive histogram equalization (CLAHE) approach. The optimized brightness compensation technique, which uses particle swarm optimization (PSO), is applied to the resolution and contrast-enhanced image to obtain uniform brightness distribution over the entire image. The proposed approach is tested on three data sets of images and it is found that the visual results, as well as the resolution and contrast levels of the tested images, are significantly superior to subjective and objective results.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

For many applications, identifying the gender information of a speaker is important. In this paper, we implemented the system which identifies the speaker and also gender of the speaker by using MFCC and GMM in an uncontrolled environment. In this text independent system, we aim on the classification using GMM for the extracted features using MFCC and also the speech signal is processed with Voice Activity Detector (VAD). In the experiments using locally recorded database, the system without voice activity detector (VAD) does not provide accurate results. So, the main aim of this paper is to develop a text independent speaker identification and also gender identification using MFCC along with VAD and GMM which improves the performance further relatively when compared with the system without VAD. The performance of the proposed system tested for 70 speakers with 100 percent recognition rate is achieved based on the log likelihood scores.

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