The field of Image Processing refers Essay Example

further categorize these enhancements. The objective is to improve visibility of information within an image through various methods such as histogram equalization, unsharp masking, convolution, highpass filtering, lowpass filtering, mathematical processes like dilation and erosion, as well as noise filtering. Histogram equalization redistributes strengths across an image's full range possible strengths while unsharp masking highlights changes in strength by subtracting a smoothed image from the original one. Convolution applies 3-by-3 masks on pixel neighborhoods. Highpass filtering emphasizes areas with rapid changes in strength while lowpass filtering smooths images and blurs regions with quick alterations.Mathematical processes have different functions in image processing.Adding images combines them pixel by pixel while subtracting images subtracts the second image from the first on a per-pixel basis.Exponential or logarithmic operations raise or take the log of pixel strength.Nonlinear enhancement or reduction of intensity fluctuations across the image can also be achieved. Scalar operations allow for individual application of user-specified constant values to each pixel, enabling uniform or non-uniform scaling of strengths. Morphological operations such as dilation and erosion enlarge and shrink bright portions of the image respectively. Noise filtering techniques aim to minimize statistical differences in the data distribution of the image in order to decrease noise.

The Adaptive Smoothing filter adjusts pixel strengths based on their original value, desired value, and level of noise, effectively reducing statistical noise particularly single-dependent noise. The Median filter sets pixels' strengths equal to the average strength of nearby pixels. The Sigma filter removes spikes in strength by setting pixel strength to the mean of surrounding strengths within two standard deviations, making it suitable for signal-independent noise removal.

Our proposed cloud computing model for image processing involves

transferring the task from a client machine to a server machine. The process begins with the client sending both the image and processing request to the server machine, which then performs image processing and sends back the output to the client. Currently, all processing is handled by the client machine itself, resulting in increased overhead. However, we plan to implement improvements to address this issue.The text describes improvements in digital image processing, specifically the elimination of noise through enhancement techniques like filtering. These techniques also enhance sharpness and smoothness. The system operates on a client-server model where clients send requests and images to be processed by the server computer. The server receives and processes the image based on the client's request before sending back the result to reduce load on the client machine.

In terms of image processing methodology, various enhancement techniques such as noise filtering, image sharpening, and smoothening are utilized with references. Certain features have also been developed as part of this project for incorporation into the system.

To successfully complete this project, a minimum system with specific specifications is required:
SOFTWARE ENVIRONMENT:
- Operating system: Windows 98/XP or later versions
- Tool: Java Frames

HARDWARE ENVIRONMENT:
- Processor: Pentium III
- RAM: 64 MB
- Harddisk: 2 GB1GB
- Processor velocity: 512 MHZ

The user/client can select an image through a graphical user interface (GUI). The request function on the client-side generates a petition message for the server. On the server-side, the procedure function processes the received image. The answer function on the server side then sends back the processed result tothe client machine.

Overall, in this setup, a waiter machine receives a petition fromthe client, processes it,and returns a response withthe processed

result.
The primary objective of this project is to enhance images through the utilization of various filtering techniques, including spacial sphere filters and frequency sphere filters. Spacial sphere filters are effective in eliminating noise and blurriness from images, while frequency sphere filters focus on enhancing internal details. The project follows a client-server model, where clients submit their images to be processed by servers. Filters have numerous applications in fields like medical diagnosing, army operations, and industrial sectors (Gonzalez.Rafael;Steve Eddins, 2008). There are several recommended books on digital image processing that can be consulted for further information. These include "Digital Image Processing (2nd ed.)" by Tinku Acharya and Ajoy K. Ray, published by Mc Graw Hill on page 163; "Image Processing – Principles and Applications" by Wilhelm Burger and Mark J. Burge, published by Wiley InterScience in 2007; "Digital Image Processing: An Algorithmic Approach Using Java" by R. Fisher.K., published by Springer with the ISBN 1-84628-379-5; "Digital Image Processing" by Milan Sonka, Vaclav Hlavac and Roger Boyle published by PWS Publishing in 1999; and "Computer Vision and Image Processing" by Tim Morris published by Palgrave Macmillan in 2004.