Multi-Sensor Image Fusion for Impulse Noise Reduction in Digital Images

Abstract - This paper introduces the concept of Multi-sensor image fusion technique for impulse noise reduction in digital images. Image fusion is the process of combining two or more images into a single image while retaining the important features of each image. Multiple sensor image fusion is an important technique used in military, remote sensing and medical applications. The images captured by five different sensors undergo filtering using five different vector median filtering algorithms and the filtered images are fused into a single image, which combines the uncorrupted pixels from each one of the filtered image. The fusion algorithm is based on quality assessment of the spatial domain from the individual de-noised images. The performance evaluation of our algorithm is evaluated using PSNR between original image and individually filtered and the fused image. Experimental results show that this fusion algorithm produce a high quality image compared to individually de-noised images

Image Fusion Algorithm for Impulse Noise Reduction in Digital Images

This paper introduces the concept of image fusion technique for impulse noise reduction in digital images. Image fusion is the process of combining two or more images into a single image while retaining the important features of each image. Multiple image fusion is an important technique used in military, remote sensing and medical applications. The images captured by different sensors undergo filtering using pixel restoration median filter and the filtered images are fused into a single image, which combines the uncorrupted pixels from each one of the filtered image The fusion algorithm is based on selecting the sharper regions from the individual de-noised images. The performance evaluation of the fusion algorithm is evaluated using structural similarity index (SSIM) between original and fused image. Experimental results show that this fusion algorithm produce a high quality image compared to individually de-noised images

Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising

Magnetic resonance imaging (MRI) is extensively exploited for more accuratepathological changes as well as diagnosis. Conversely, MRI suffers from variousshortcomings such as ambient noise from the environment, acquisition noise from theequipment, the presence of background tissue, breathing motion, body fat, etc.Consequently, noise reduction is critical as diverse types of the generated noise limit the efficiency of the medical image diagnosis. Local polynomial approximation basedintersection confidence interval (LPA-ICI) filter is one of the effective de-noising filters.This filter requires an adjustment of the ICI parameters for efficient window size selection.From the wide range of ICI parametric values, finding out the best set of tunes values is itselfan optimization problem. The present study proposed a novel technique for parameteroptimization of LPA-ICI filter using genetic algorithm (GA) for brain MR imagesde-noising. The experimental results proved that the proposed method outperforms theLPA-ICI method for de-noising in terms of various performance metrics for different noisevariance levels. Obtained results reports that the ICI parameter values depend on the noisevariance and the concerned under test image

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications