Detail-preserving switching algorithm for the removal of random-valued impulse noise

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tri-directional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities

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

Fuzzy techniques for noise removal in image sequences and interval-valued fuzzy mathematical morphology

Image sequences play an important role in today's world. They provide us a lot of information. Videos are for example used for traffic observations, surveillance systems, autonomous navigation and so on. Due to bad acquisition, transmission or recording, the sequences are however usually corrupted by noise, which hampers the functioning of many image processing techniques. A preprocessing module to filter the images often becomes necessary. After an introduction to fuzzy set theory and image processing, in the first main part of the thesis, several fuzzy logic based video filters are proposed: one filter for grayscale video sequences corrupted by additive Gaussian noise and two color extensions of it and two grayscale filters and one color filter for sequences affected by the random valued impulse noise type. In the second main part of the thesis, interval-valued fuzzy mathematical morphology is studied. Mathematical morphology is a theory intended for the analysis of spatial structures that has found application in e.g. edge detection, object recognition, pattern recognition, image segmentation, image magnification… In the thesis, an overview is given of the evolution from binary mathematical morphology over the different grayscale morphology theories to interval-valued fuzzy mathematical morphology and the interval-valued image model. Additionally, the basic properties of the interval-valued fuzzy morphological operators are investigated. Next, also the decomposition of the interval-valued fuzzy morphological operators is investigated. We investigate the relationship between the cut of the result of such operator applied on an interval-valued image and structuring element and the result of the corresponding binary operator applied on the cut of the image and structuring element. These results are first of all interesting because they provide a link between interval-valued fuzzy mathematical morphology and binary mathematical morphology, but such conversion into binary operators also reduces the computation. Finally, also the reverse problem is tackled, i.e., the construction of interval-valued morphological operators from the binary ones. Using the results from a more general study in which the construction of an interval-valued fuzzy set from a nested family of crisp sets is constructed, increasing binary operators (e.g. the binary dilation) are extended to interval-valued fuzzy operators

Field Programmable Gate Array based Readout for Surface Acoustic Wave Portable Gas Detector

Surface acoustic wave (SAW) is one of the most promising technology in the field of gas sensing at low concentrations. Field deployable portable SAW detectors are, however, prone to noise, there by limiting the detection at low concentrations. To meet the current requirements of gas detection at low concentrations, the readout methodology needs to be based on minimal hardware and better noise management. In this paper we describe a readout scheme for portable SAW gas detectors incorporating a field programmable gate array (FPGA). The developed readout system includes a modified reciprocal frequency counter for differential SAW sensor, median noise filtering and moving averages smoothing for noise management, peak detection and interfacing with external display, all implemented in FPGA. The developed readout was tested against VOCs using a lab developed vapour generator and the results have been presented in the paper. The readout system is compact, low power consuming and expandable through software thus ideal for portable handheld applications

Portal Imaging Using a CSI (TL) Scintillator Coupled to a Cooled CCD Camera

The purpose of this research was to design a high performance digital portal imaging system, using a transparent x-ray scintillator coupled to a cooled CCD camera. Theoretical analysis using Monte Carlo simulation was performed to calculate the QDE, SNR and DQE of the system. A prototype electronic portal imaging device (EPID) was built, using a 12.7 mm thick, 20.32 cm diameter, CsI (Tl) scintillator, coupled to an Astromed ® liquid nitrogen cooled CCD TV camera. The system geometry of the prototype EPID was optimized to achieve high spatial resolution. Experimental evaluation of the prototype EPID was performed, by determining its spatial resolution, contrast resolution, depth of focus and light scatter. Images of phantoms, animals and human subjects were acquired using the prototype EPID and were compared with those obtained using conventional and high contrast portal film and a commercial EPID. An image processing protocol was developed. The protocol was comprised of preprocessing, noise removal and image enhancement algorithms. An adaptive median filter algorithm for the removal of impulse noise was developed, analyzed and incorporated into the image processing protocol. Results from the theoretical analysis and experimental evaluation have indicated that the performance of the CsI (Tl) - CCD system is comparable or superior to that of current commercial and experimental portal imaging technologies, such as high contrast portal film, commercial TV camera based EPIDs, and amorphous silicon based flat panel EPIDs

CORRELATION COEFFICIENT BASED DETECTION ALGORITHM FOR REMOVAL OF RANDOM VALUED IMPULSE NOISE IN IMAGES

This paper aims to present an alternative and novel method for removal of Random Valued Impulse Noise in corrupted images which is a challenging task as compared to the removal of fixed valued impulse noise. The proposed algorithm i.e. “Correlation Coefficient Based Detection Algorithm” (CCBD) is a two stage filter. The Detection stage of CCBD utilises the Correlation Coefficients of the absolute differences of the pixels in detection window with their Mean, the Central Pixel and a predefined value respectively. The Filtering stage of CCBD utilises the Fuzzy Switching Weighted Median filter (FSWM) for restoration of the corrupted image. The performance of CCBD has been compared to some of the existing methods e.g. Rank Order Absolute Difference (ROAD), Rank Order Logarithmic Difference (ROLD), Triangle Based Linear Interpolation (TBLI) and Adaptive Switching Median (ASM) algorithms. The Comparative analysis in terms of MSE, PSNR and SSIM show that the CCBD is superior to the existing methods in all parameters