The field of Image Processing refers to processing digital images by means of digital computer. One of the main application areas in Digital Image Processing methods is to improve the pictorial information for human interpretation. Most of the digital images contain noise. This can be removed by many enhancement techniques. Filtering is one of the enhancement techniques which is used to remove unwanted information (noise) from the image. It is also used for image sharpening and smoothening. Some
neighborhood operations work with the values of the image pixels in the neighborhood and the corresponding values of a sub image that has the same dimensions as the neighborhood. The sub image is called a “filter”. The aim of this project is to demonstrate the filtering techniques by performing different operations such as smoothening, sharpening, removing the noise etc. This project has been developed using Java language because of its universal acceptance and easy understandability. The Image Processing is based on client-server model.
A client sends a request with image that is to be processed to the server computer. The server computer receives the image and process it according to client request and sends the result back to the client machine. Keywords— Image Processing, Human Interpretation, Filtering, Smoothening \ INTRODUCTION Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transformation, and representation for autonomous machine perception.
An image may be defined as a two-dimensional function, f(x , y), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (x , y) is called the intensity or gray level of the image at the point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of digital computer. Digital image is composed of finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels.
Pixel is the term most widely used to denote the elements of a digital image. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. Filters are one of digital image enhancement technique used to sharp the image and to reduce the noise in the image. There are two types of enhancement techniques called Spatial domain and Frequency domain techniques which are categorized again for smoothing and sharpening the images. LITERATURE SURVEY AND OUTCOME The Enhancement Techniques make information more visible.
The various types of image processing techniques are as follows. A. Histogram equalization- Redistributes the intensities of the image of the entire range of possible intensities (usually 256 gray-scale levels). Unsharp masking-Subtracts smoothed image from the original image to emphasize intensity changes. B. Convolution- It is a technique in which 3-by-3 masks operating on pixel neighborhoods. Highpass filter-Emphasizes regions with rapid intensity changes. Lowpass filter-Smoothes images, blurs regions with rapid changes. C. Math processes- In this technique, It performs a variety of functions.
Add images-Adds two images together, pixel-by-pixel. Subtract images-Subtracts second image from first image, pixel by pixel. Exponential or logarithm-Raises e to power of pixel intensity or takes log of pixel intensity. Nonlinearly accentuates or diminishes intensity variation over the image. Scaler add, subtract, multiply, or divide-Applies the same constant values as specified by the user to all pixels, one at a time. Scales pixel intensities uniformly or non-uniformly Dilation-Morphological operation expanding bright regions of image. Erosion-Morphological operation shrinking bright regions of image.
D. Noise filtering- It decreases noise by diminishing statistical deviations. Adaptive smoothing filter-Sets pixel intensity to a value somewhere between original value and mean value corrected by degree of noisiness. Good for decreasing statistical, especially single-dependent noise. Median filter-Sets pixel intensity equal to median intensity of pixels in neighborhood. An excellent filter for eliminating intensity spikes. Sigma filter-Sets pixel intensity equal to mean of intensities in neighborhood within two of the mean. Good filter for signal-independent noise. PROBLEM FORMULATION AND METHODOLOGY
The System Model We consider a cloud computing model for image processing system. The system will be designed in such a way that the processing of image is performed on server machine rather than client machine. In this, client sends the image with its required request of processing to server machine to process it accordingly. The server machine receives the request and process it and finally send back the result to client machine. Existing System: In the Existing System, A number of image processing techniques, in addition to enhancement techniques, can be applied to improve the data usefulness.
Techniques include convolution edge detection, mathematics, filters, trend removal, and image analysis. The Image processing is performed to client computer itself so the overhead to client computer increases due to processing of Image. Proposed System: The proposed system can be summarized as the following three aspects: Most of the digital images contains noise. This can be removed by many enhancement techniques. Filtering is one of the enhancement techniques which is used to remove unwanted information (noise) from the image. It is also used for image sharpening and smoothening..
The Image Processing is based on client-server model. A client sends a request with image that is to be processed to the server computer. The server computer receives the image and process it according to client request and sends the result back to the client machine. The image processing is performed on server computer so there is much less overhead on client computer to process an image. Work done In Image processing methodology, we study the different types of enhancement techniques like noise filtering, image sharpening, image smoothening etc. with the help of different references.
Now finally we concluded how to complete this project and we prepared some modules that will be present in our project. And to complete this project we require minimum system requirement and project specification as follows: SOFTWARE ENVIRONMENT: Operating system: windows 98/XP or later versions Tool: Java Frames HARDWARE ENVIRONMENT: Processor : Pentium III RAM : 64 MB Harddisk : 2. 1GB Processor speed : 512 MHZ Modules: User/client: In this module user selects an image through GUI. Request: It is a module that belongs to client side that generate request message for server.
Process: It’s the module lying on server side that processes the image sent by the client. Reply: It is also a server site module that forward the result after processing of element to client machine. Server: In this module, server machine receives the request from client process it and reply back the result to client. CONCLUSIONS The objective of the project is to smooth and sharp the images by using various Filtering techniques. Where Filtering techniques are one of the enhancement techniques in the Digital image processing. Here in the project
I had implemented few spatial domain filters and frequency domain filters. Where spatial domain filters removes the noise and blurs the image. And frequency domain filters are used to sharpen the inside details of an image. The Image Processing is based on client-server model. A client sends a request with image that is to be processed to the server computer. The server computer receives the image and process it according to client request and sends the result back to the client machine. Filters are useful in many application areas as medical diagnosis, Army and Industrial areas. REFERENCES
Gonzalez, Rafael; Steve Eddins (2008). “4”. Digital Image Processing (2nd ed. ). Mc Graw Hill. p. 163. Tinku Acharya and Ajoy K. Ray (2006). Image Processing – Principles and Applications. Wiley InterScience. Wilhelm Burger and Mark J. Burge (2007). Digital Image Processing: An Algorithmic Approach Using Java. Springer. ISBN 1-84628-379-5. R. Fisher, K (2002). Digital Image Processing. Springer. ISBN 3-540-67754-2. Milan Sonka, Vaclav Hlavac and Roger Boyle (1999). Image Processing, Analysis, and Machine Vision. PWS Publishing. Tim Morris (2004). Computer Vision and Image Processing. Palgrave Macmillan.
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