Even after your best efforts you couldn't get the best Quality Image!! and you are wishing to have a System to be smart enough to detect Noisy Image and implement the best Noise Removal Algorithm to give the User High Quality Images. What if I say that it's Possible. That's our Project!!
Contents
- Objective
- Conference Paper
- Languages
- Accessing the Program
- Why do we need such a System
- Contributors
The Main Objective of the Project is to help the user to judge the best Noise Removal Algorithm for a particular image accessed over an Graphical UI that makes it is easy to use and gives results as needed.
Conference Paper of the same has written and published to IJCA (Internal Journal of Computer Applications): Digital Library URI : http://www.ijcaonline.org/proceedings/icrdsthm2017/number1/29309-7006 ISBN : 973-93-80975-26-2
Developed with languages and frameworks of C, GTK and OpenGL and works with 16bit bitmap image format when passed to the system gives the result across 6 different Noise Removal Algorithms implemented in the system that provides statistical Graphic result over commonly found Salt & Pepper and Guassian Noise Models.
- Use any IDE like CodeBlocks and integrate C language in it
- Integrate GTK Platform to the System
- As well as Integrate OpenGL Platform to the CodeBlocks IDE Environment
- Then implement the main file in the system and run the code
Ever wonder why the images you capture on your Phone or DSLR's get Noisy Images even when you used tripod or used other safety precautions. It's not your fault but it might be the defect or aging of Semi-Conductor material or disturbances in the Environment that creates such images. Thus Noise Removal Algorithms could come in handy. So in that scenario, there isn't any particular Algorithm that could be implemented in any Noisy Case. So our System would automatically detect which Algorithm gives the best result and applies that Algorithm on the Image thus producing better Quality Images on the Go.
Any user using this Repo should include this Git link and Contributor's name in their work.
K.N.Dheeraj ([email protected]) Chaitanya Subhedar ([email protected]) Jayakrishnan Ashok ([email protected]) Parth Gupta ([email protected])
