Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level

An Efficient Approach of Removing the High Density Salt

Images are often corrupted by impulse noise, also known as salt and pepper noise. Salt and pepper noise can corrupt the images where the corrupted pixel takes either maximum or minimum gray level. Amongst these standard median filter has been established as reliable - method to remove the salt and pepper noise without harming the edge details. However, the major problem of standard Median Filter (MF) is that the filter is effective only at low noise densities. When the noise level is over 50% the edge details of the original image will not be preserved by standard median filter. Adaptive Median Filter (AMF) performs well at low noise densities. In our proposed method, first we apply the Stationary Wavelet Transform (SWT) for noise added image. It will separate into four bands like LL, LH, HL and HH. Further, we calculate the window size 3x3 for LL band image by Reading the pixels from the window, computing the minimum, maximum and median values from inside the window. Then we find out the noise and noise free pixels inside the window by applying our algorithm which replaces the noise pixels. The higher bands are smoothing by soft thresholding method. Then all the coefficients are decomposed by inverse stationary wavelet transform. The performance of the proposed algorithm is tested for various levels of noise corruption and compared with standard filters namely standard median filter (SMF), weighted median filter (WMF). Our proposed method performs well in removing low to medium density impulse noise with detail preservation up to a noise density of 70% and it gives better Peak Signal-to-Noise Ratio (PSNR) and Mean square error (MSE) values

An Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT)

Impulse noise in images is present because of bit errors in transmission or introduced during the signal acquisition stage. There are two types of impulse noise, they are salt and pepper noise and random valued noise. In our proposed method, first we apply the Stationary wavelet transform for noise added image. It will separate into four bands like LL, LH, HL and HH. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 02019;s and 2552019;s are present in the selected window and when all the pixel values are 02019;s and 2552019;s then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard median filter (MF), decision based algorithm (DBA). The proposed method performs well in removing low to medium density impulse noise with detail preservation up to a noise density of 70% and it gives better Peak signal-to-noise ratio (PSNR) and mean square error (MSE) values

Hybridization of Algorithm for Restoration of Impulse Noise Image

AbstractIn today's era, mainly communication is done through visual communication. Almost all information is transmitted in the form of digital image or video. But after transmission, the obtained information is often corrupted with noise. At a high noise density, detail information of the image is hidden by noise. Hence, we have to recover the original image by removing noise of the image without loss of data. Here, we proposed two hybridization methods, to yield better restoration of impulse noise images. The first proposed algorithm has hybridization of Decision Based algorithm along with 2D discrete wavelet transform. In second, hybridization of Decision based algorithm with Adaptive Wiener Filter. Experimental results in Figs. 3–6 shows that proposed algorithm 1 outperform in terms of visual quality till 80% of noise density. Proposed algorithm 2 also performs excellent in term of visual quality, but within noise density ranges from 70% to 90%. Even at 95% noise density, proposed algorithm 2 gives an improvement in PSNR value from 5.29dB to 18.98dB. Mean Absolute Error, Mean Square Error, Peak Signal to Noise Ratio and Image Enhancement Factor are calculated and the comparative study is made between Standard Median Filter, Cascaded decision based algorithm proposed in10, and proposed algorithm

Impulse Noise Removal Using Soft-computing

Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation

An Efficient Threshold Based Mixed Noise Removal Technique

Removing or reducing noises from image is very important task in image processing. This paper presents an efficient noise removal technique to restore original digital images corrupted by mixed noise. The proposed filtering technique consists of three steps: noisy pixel detection using fuzzy flag, mixed noise filtering step and calculating threshold value remove the pixel value with replacement conditions. Noises in this methodology are the combination of gaussian noise and salt and pepper noise. This methodology reduces the mixed noise without lossing edges sharpness and information. This methodology gives better results existing many fuzzy algorithms. The proposed technique shows better peak signal noise ratio result with thresholding replacement conditions. Hence, this mixed noise removal technique finds application in numerous segments of image process like digital tv, medical image process, camera, police work systems etc. Wiener filter is used for image enhancement