Image Contrast Enhancement with Brightness Preserving Using Feed Forward Network

Image improvement techniques are very useful in our daily routine.In the field of image enhancement Histogram Equalization is a very powerful, effective and simple method. Histogram Equalization (HE) is a popular, simple, fast and effective technique for improving the gray image quality. Contrast enhancement was very popular method but it was not able to preserve the brightness of image. Image Dependent Brightness Preserving Histogram Equalization (IDBPHE) technique improve the contrast as well as preserve the brightness of a gray image. Image features Peak Signal to Noise Ratio (PSNR) and Absolute Mean Brightness Error (AMBE) are the parameters to measure the improvement in a gray image after applying the algorithm. Unsupervised learning algorithm is an important method to extract the features of neural network. We propose an algorithm in which we extract the features of an image by unsupervised learning. After apply unsupervised algorithm on the image the PSNR and AMBE features are improved

Image Contrast Enhancement with Brightness preserving using Curvelet Transform and Multilayer Perceptron

Image Improvement Techniques Are Veryuseful In Our Daily Routine. In The Field Ofimage Enhancement Histogram Equalizationis A Very Powerful, Effective And Simplemethod. But In Histogram Equalizationmethod The Brightness Will Disturb Whileprocessing. Original Image Brightnessshould Be Kept In The Processed Image. Soimage Contrast Must Be Enhanced Withoutchanging Brightness Of Input Image. In Ourproposed Method Of Image Contrastenhancement With Brightness Preservingusing Curvelet Transform And Multilayerperceptron We Will Solve This Problem Andget Better Result Than Existing Methods.Results Are Compared On The Basis Of Twoimportant Parameter For Image Quality Suchas Absolute Mean Brightness Error (Ambe)And Peak Signal To Noise Ratio (Psnr)