A Study on Digital Images and Their Related Methods

Image Processing is a technique to improve raw images received from cameras or sensors placed on satellites, space probes and aircrafts or pictures taken in normal life for various applications. Various techniques have been developed in Image Processing during the last five decades. Most of the techniques are developed for enhancing images obtained from unmanned spacecrafts, space probes and military inspection flights. Image Processing systems are becoming popular due to easy availability of powerful personnel computers, large size memory devices, graphics software. Image Processing is used in various applications such as Remote Sensing, Medical Imaging, Textiles, Material Science, Military, Film industry, Document processing , Graphic arts The common steps in image processing are image scanning, storing, enhancing and interpretation.

Image Processing are given two methods as follow:

Processing Analog Image Processing is refer to the alteration of image through electrical means such example is the television image. The television signal is a voltage level which varies in amplitude to represent brightness through the image. By electrically varying the signal, the displayed image appearance is altered. The brightness and contrast controls on a TV set serve to adjust the amplitude and reference of the video signal, resulting in the brightening, darkening and alteration of the brightness range of the displayed image.

In this case, digital computers are used to process the image. The image will be converted to digital form using digital conversion method and then process it. It is defined as the subjecting numerical representations of objects to a series of operations in order to obtain a desired result. It starts with one image and produces a modified version of the same. It is therefore a process that takes an image into another. dimensional data. A digital image is an array of actual numbers represented by a finite number of bits. The principle advantage of Digital Image Processing methods is its versatility, repeatability and the preservation of original data precision.

An image defined as in the "real world" is considered to be a function of two real variables, such example, f(x,y) with f as the amplitude of the image at the real coordinate position (x,y). An image processing operation typically defines a new image g in terms of an existing image f. The effect of digitization is given figure.

Image enhancement techniques can be divided into two wide categories: 2. Frequency domain methods, which function on the Fourier transform of an image.

The value of a pixel with coordinates (x,y) in the enhanced image is the result of performing some operation on the pixels in the neighborhood of (x,y) in the input image, F. Neighborhoods can be any shape, but usually they are rectangular.

Histogram equalization is a common technique for enhancing the appearance of images. Suppose we have an image which is predominantly dark. Then its histogram would be skewed towards the lower end of the grey scale and all the image detail is compressed into the dark end of the histogram. If we could `stretch out' the grey levels at the dark end to produce a more uniformly distributed histogram then the image would become much clearer.

Image enhancement in the frequency domain is straightforward. We simply compute the Fourier transform of the image to be enhanced, multiply the result by a filter and take the inverse transform to produce the enhanced image. The idea of blurring an image by reducing its high frequency components or sharpening an image by increasing the magnitude of its high frequency components is intuitively easy to understand. However, computationally, it is often more efficient to implement these operations as convolutions by small spatial filters in the spatial domain. Understanding frequency domain concepts is important, and leads to enhancement techniques that might not have been thought of by restricting attention to the spatial domain. frequency components in the image. It results in blurring of the image .An ideal low pass filter would retain all the low frequency components, and eliminate all the high frequency components. However, ideal filters suffer from two problems: blurring and ringing. These problems are caused by the shape of the associated spatial domain filter, which has a large number of undulation. Smoother transitions in the frequency domain filter, such as the Butterworth filter, achieve much better results.

The objective of image compression is to reduce the size of digital images to save storage space and transmission time. Lossless compression is preferred for artificial images like technical drawings, icons and also be preferred for high value content, such as medical imagery or image scans made for archival purposes. Lossy methods are especially suitable for natural images such as photos in applications where minor loss of fidelity is acceptable to achieve a substantial reduction in bit rate. The lossy compression that produces unnoticeable differences can be called visually lossless. Run-length encoding, Huffman encoding and Lempel Ziv encoding are the methods for lossless image compression. Transform coding such as DCT, Wavelet transform are applied followed by quantization and symbol coding can be cited as a method for lossy image compression. primarily grouped into some categories such as Image Rectification, Enhancement and Information withdrawal. Image rectification is the pre-processing of satellite data for geometric and radiometric connections. Enhancement is applied to image data in order to effectively display data for subsequent visual interpretation. Information withdrawal is based on digital classification and is used for generating digital thematic map. This paper presents various techniques of image processing. These are still a challenging task for the researchers and academicians. Comparing the performance of image processing technique is difficult unless identical data sets and performance measures are used. Studied in different papers related to some image processing techniques are used for better technique for different types of data inputs. There are different types of symbol coding techniques that can be used for image processing. After study of all techniques it is found that lossless image compression techniques are most effective over the lossy compression techniques.

[1]. Digital Image Processing - A Remote Sensing Perspective, Jhon R. Jenson, 3 rd Edition, Prentice – Hall, 2013. [2]. Digital Image Processing - Kenneth R. Castleman, Prentice-Hall, 2015. [3]. KMM Rao, Medical Image Processing, Proc. of workshop on Medical Image Processing and Applications, 8th October 2014 @ NRSA, Hyderabad-37. [4]. KMM Rao, Image Processing for Medical Applications, Proc. of 14th world conference on NDT, 8th – 13th Dec 2014. [5]. Ramanjaneyulu M, KMM Rao , A Novel technique to Resample High Resolution Remote Sensing Satellite Images, Proc. of IGRASS-02, Colorado. [6]. KMM et al., Design and Fabrication of Color Scanner, Indian Journal of Technology, Vol 15, Apr 2014. [7]. Fundamentals of Digital Image Processing - Anil K. Jain, Prentice-Hall, 2013. [8]. Computer Image Processing and Recognition - Ernest L. Hal, Academic Press, 2012. [9]. Digital Image Processing - Chellappa, 2nd Edition, IEEE Computer Society Press, 2014.

Research Scholar, OPJS University, Churu, Rajasthan