Image enhancement is a fundamental pre-processing step for many automated systems and vision systems. Many enhancement algorithms have been anticipated based on different sets of criteria. One of the most widely used algorithms is the direct multi-scale image enhancement algorithm. The specialty of this algorithm is, it provides contrast enhancement, tonal rendition, dynamic range compression and accurate edge preservation of the images. It also provides these features to the individual images and/or simultaneously to the images. In this proposed method, a multi-scale image enhancement algorithm is established by using parametric contrast measure with the transform techniques such as Laplacian pyramid, discrete wavelet transform, Stationary wavelet transform and Dual-tree complex wavelet transform. The new contrast measure provides both the luminance and contrast masking characteristics of the human visual system. The proposed method is used to attain simultaneous local and global enhancements. The enhancement measures such as Entropy, Mean opinion score and Measure of enhancement gives better results than the existing methods.

As a consequence of the fact, transmitting data has been fast and easy these days due to the development of the Internet. Where internet is the most important medium for confidential and non-confidential communications. Security is the major matter for these communications and steganography is the art of hiding and transmitting secret messages through carriers without being exposed. This paper presents a secured model for communication using image steganography. The main concern is to create a Java-based tool called IMStego that hides information in images using Least Significant Bit (LSB) algorithm (1-LSB) and modified Least Significant one Bit algorithm, i.e. Least Significant 2 Bits algorithm (2-LSB). IMStego is a more comprehensive security utility where it provides user-friendly functionality with interactive graphical user interface and integrated navigation capabilities. It provides the user with two operations, which are hiding secret data into images and extracting hidden data from images using 1-LSB or 2-LSB algorithm. IMStego tool hides secrete information in color static images with formats BMP and PNG. 

To develop a Braille recognition system, it is required to have the stored images of Braille sheets. This paper describes a method and also the challenges of building the corpora for Hindi Devanagari Braille. A few Braille databases and commercial software's are obtainable for English and Arabic Braille languages, but none for Indian Braille which is popularly known as Bharathi Braille. However, the size and scope of the English and Arabic Braille language databases are limited. Researchers frequently develop and self-evaluate their algorithm based on the same private data set and report its behavior using ad-hoc measures of performance. There is no well-defined benchmark database for comparative performance evaluation of results obtained. The developed Braille database, Bharati Braille-Bank, is a large and well characterized information of Braille documents and its related data for use by the research community working in Optical Braille Recognition (OBR) for Bharati Braille. In the present form it includes databases of embossed double sided, embossed single sided, skewed, Hand punched and images with varying resolutions of Hindi Braille. The objective of this work is to stimulate current research and new investigations in the study of Hindi OBR. Without common databases such as those provided by Braille Bank it is impossible to resolve certain contradictory research results. To overcome this problem, Braille Bank provides facilities for the comparative analysis of the data and the evaluation of proposed algorithms with the standard database. In addition, it provides free access to the developed database in the form of Compact Disc-Read Only Memory (CD-ROM). This work is a step forward in the direction of development of standards for Hindi Devanagari Braille data collection for Indian languages. The mission of the resource is to accelerate the development of OBR for Bharati Braille. 

In mobile devices, perceived speech signal deteriorates significantly in the presence of near-end noise as the signal arrives directly at the listener's ears in a noisy environment. There is an inherent need to increase the clarity and quality of the received speech signal in noisier environment. It is accomplished by incorporating speech enhancement algorithms at the receiver end. The objective is to improve the intelligibility and quality of the speech signal by dynamically enhancing the speech signal when the near-end noise dominates. This paper proposes a speech enhancement approaches by inculcating the threshold of hearing and auditory masking properties of the human ear. Incorporating the masking properties, the speech samples that are audible can be obtained. In low SNR environments, selective audible samples can be enhanced to improve the clarity of the signal rather than enhancing every loud sample. Intelligibility and quality of the enhanced speech signal are measured using Speech Intelligibility Index and Perceptual Evaluation of Speech Quality. Experimental results connote the intelligibility and quality improvement of the speech signal with the proposed method over the unprocessed far-end speech signal. This approach is efficient in overcoming the deterioration of speech signals in a noisy environment.

Copy move forgery detection is a very popular research area and a lot of methods have been suggested by researchers. However, every method has its own merits and weaknesses and hence, new techniques are being continuously devised and analyzed. There are many post processing operations used by the manipulators to obstruct the forgery detection techniques. One such operation is changing the contrast of the whole image or copy moved regions, which many existing methods fail to address. A novel method using binary discrete cosine transform vectors is proposed to detect copy move forgery in the presence of contrast changes. The image is divided into overlapping blocks and DCT coefficients are calculated for these blocks. Feature vectors are created from these blocks using signs of the DCT coefficients. Coefficient of correlation is used to match resulting binary vectors. The experiments show that the proposed method is able to detect copy move forgery in presence of contrast changes. The proposed method is also invariant to other post processing operations like Gaussian noise, JPEG compression and little rotation and scaling. 

Accurate detection of exudates in the diabetic retinal images is a challenging task. The images can have varying contrast and color characteristics. In this paper authors present the performance comparison of two feature extraction methods namely color intensity features and second order texture features based on GLCM. Authors have proposed and implemented new approach for GLCM feature calculation in which the input image is divided into number smaller blocks and GLCM features are computed on these blocks. The performance of each feature extraction method is evaluated using Back Propagation Neural Network (BPNN) classifier that is classifying the blocks as either abnormal block or normal block. With GLCM features, an accuracy of 76.6% was obtained and with color features an accuracy of 100% was obtained. It was found that color features are better in identifying true positives than GLCM based texture features. However use of GLCM features reduces the occurrence of false positives.

Efficient diagnosis plays a crucial role for treatment. In many cases of criticalness, radiologists, doctors prefer to the usage of internet technologies in order to search for similar cases. Accordingly in this paper an effective mechanism of Content Based Image Retrieval (CBIR) is presented, which helps the radiologists/doctors in retrieving similar images from the medical dataset. The paper is presented by considering brain medical images from a medical dataset. Feature vectors are to be extracted efficiently so as to retrieve the images of interest. In this paper a two-way approach is adopted to retrieve the images of relevance from the dataset. In the first step the Probability Density Functions (PDF) are extracted and in the second step the relevant images are extracted using correlation coefficient. The accuracy of the model is tested on a database consisting of 1000 MR images related to brain. The effectiveness of the model is tested using Precision, Recall, Error rate and Retrieval efficiency. The performance of the proposed model is compared to Gaussian Mixture Model (GMM) using quality metrics such as Maximum distance, Mean Squared Error, Signal to Noise Ratio and Jaccard quotient.

In this paper an object tracking system with utilizing optical flow technique, and Gradient Vector Flow (GVF) active contours is presented. Optical flow technique is less sensitive to background structure and does not need to build a model for the background of image so it would need less time to process the image. GVF active snakes have good precision for image segmentation. However, due to the high computational cost, they are not usually applicable. Since precision and time complexity are the most important factors in the image segmentation, several methods have been developed to overcome these problems. In this paper, we, first, recognize the moving object. Then, the object fame with some pixels surrounding to it, was created. Then, this new frame is sent to the GVF filed calculation procedure. Contour initialization is obtained based on the selected pixels. This approach increases the calculation speed, and therefore makes it possible to use the contour for the tracking. The system was built, and tested with a microcomputer. The results show a speed of 10 to 12 frames per second which is considerably suitable for object tracking approaches.