Digital Processing

Character recognition, usually abbreviated to optical character recognition or shortened OCR, is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text. It is a field of research in pattern recognition, artificial intelligence and machine vision. Though academic research in the field continues, the on character recognition has For many document-input tasks, character recognition is the most cost-effective and speedy method available. And each year, the technology frees acres of storage space once given over to file cabinets and boxes full of paper documents. The objective of this paper is to identify handwritten characters with the use of neural networks. We have to construct suitable neural network and train it properly. The program should be able to extract the characters one by one and map the target output for training purpose. After automatic processing of the image, the training dataset has to be used to train “classification engine” for recognition purpose. The program code has to be written in MATLAB and supported with the usage of Graphical User Interface (GUI).

Underwater images have a wide range of applications in different fields. But underwater images have poor visibility, low contrast and diminishing colors. These all problems erupt as a result of the haze present in underwater images which seriously lowers the quality of underwater images. So, underwater image dehazing or haze removal is important. Haze removal is a challenging and complex problem because it is based on unknown depth information. This paper has reviewed various techniques for haze removal in underwater images. Every technique has its own advantages and limitations. This paper has also discussed the methods, advantages and limitations of various underwater image haze removal techniques.

Satellite images are being widely used in many fields. One of the major issues of these types of images is their resolution. The most important quality factors of the images come from its resolution. Interpolation is a known method to increase the resolution of a digital image. A satellite image resolution enhancement technique based on the interpolation of the high-frequency sub bands obtained by discrete wavelet transform (DWT) and the input image is a new proposed technology. The proposed resolution enhancement technique uses DWT to decompose the input image into different sub bands. Then, the high-frequency sub band images and the input low-resolution image have been interpolated by using quadratic interpolation, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. By employing DWT with quadratic interpolation, it is possible to recover the high frequency components of the input image which provides with a good visual clarity on the edges of the images and thus enhanced high resolution images are obtained.

Since its inception, Color Filter Arrays (CFA) image compression has proved to be the most popular approach for image storage in single sensor camera imaging. CFA plays a very important role in the single sensor Digital Still Cameras (DSCs) to record intensity information of one color component at each pixel location. The captured image data is then compressed with different compression methods. CFA image compression introduced several innovative schemes to perform both lossy and lossless compression. Number of studies is available in the literature with respect to the performance of CFA image compression and its mechanisms. In this paper, a systematic survey of the CFA image compression in both lossy and lossless mode from 1991 to 2011 is presented. The surveyed papers discuss the coding techniques used and the resulting observations about compression performance in terms of bit rate PSNR of the reconstructed image.

Convergence plays an important role in optimization of error. In this paper, Convergence is analyzed in terms of power with respect to Root Mean Square Error. Power Adaptation Algorithms are implemented on image transmission and the convergence is analysed. Distance Based Power Adaptation Algorithm outperforms compared with the other algorithms.

Image provides an important way for medical studies. The objective of the paper is to delineate the imaging process from graph theory with computer. The aim of digital image processing is to seek for the better methods and technologies on image process by noise riding, strengthening, restoring, dividing, extracting and so on based on computer. The main facts including the development of computer science, mathematics science and the increasing of application requirements on medical science, environmental science, industry and so on. Graph theory of computer provides a basic method and process for imaging process.

We proposed a new algorithm for detection of micro calcifications using mathematical morphology, adaptive bilateral filter and radon transform. The Adaptive bilateral filter is able to smooth the noise, while enhancing edges and textures in the image. Morphology operations such as dilation, erosion, opening and closing are offering a quality extraction of Shapes. In this method the radon transform based methods is applied to extract the micro calcifications. The performance of the segmentation of micro calcification is to be improved by including the mathematical morphology operations along with Radon transform. Radon transform based methods are used in computer tomography which efficiently produced random projections and give a simultaneous translational rotational alignment in two and three dimensions. The size and shape of the lesions are measured by using canny edge detection methods and segmented results are generated. The proposed method evaluated with usefulness of the system with help expert radiologists.