PixelSciences IEEE Student Projects Centre

The image denoising is a preprocessing step in image processing and is used to recover the image which is corrupted by noise. It shown below the image corrupted by impulsive noise or salt and pepper noise is denoised by Adaptive Median filter. Impulsive noise is added to an image when the image is transmitted over a noisy channel and decoding error on the receiver side. It is modelled by imnoise function in Matlab programming. Median filter with small fixed window size is a preferred technique for denoising an image corrupted by salt & pepper noise because of simple and efficient. However, it is performance decreased the image corrupted by high density noise pixels. Adaptive Median filter changing it's window size depends on density of noise is a preferred technique for denoising an image corrupted by high density salt & pepper noise. Original Image and 70% salt & pepper noised image Denoised image by Adaptive median...

A contrast enhancement of images by histogram equalization (HE) is very popular image processing technique widely used as preprocessing steps for medical and many other image and video processing applications. HE performs its operation by remapping the gray levels of the image based on the cumulative probability distribution of the input gray levels Test image 1 : butterfly Algorithm steps Histogram Equalization set maxI=255, minI=0 set nbins =255 Read Input-Image, im hc <- histogram-count (im,nbins) prob <- (hc) / sum(hc) cprob <- cumlative-sum(prob) Tf <- minI + (maxI -minI) x cprob Eim = Tf(im) maxI - maximum intensity level , minI - maximum intensity level nbins - number of bins(level) for histograms count Tf - Intensity mapping transfer function im - Input Image Eim - Enhanced output Image Matlab code for...

It is shown below IEEE project titles of image processing on Image Enhancement, Image De-noising, Image Segmentation and Object Recognition. Image Enhancement A Dynamic Histogram Equalization for Image Contrast Enhancement. Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modelling. Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images. Contrast-Preserved Chroma Enhancement Technique using YCbCr Colour Space. Multi Segment Histogram Equalization for Brightness Preserving Contrast Enhancement. Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution. Image Denoising SUSAN controlled decay parameter adaption for non-local means image denoise. Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise. Two-Direction Nonlocal Model ...