Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive Feedforward Neural Network

The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it

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

Techniques for enhancing digital images

The images obtain from either research studies or optical instruments are often corrupted with noise. Image denoising involves the manipulation of image data to produce a visually high quality image. This thesis reviews the existing denoising algorithms and the filtering approaches available for enhancing images and/or data transmission. Spatial-domain and Transform-domain digital image filtering algorithms have been used in the past to suppress different noise models. The different noise models can be either additive or multiplicative. Selection of the denoising algorithm is application dependent. It is necessary to have knowledge about the noise present in the image so as to select the appropriated denoising algorithm. Noise models may include Gaussian noise, Salt and Pepper noise, Speckle noise and Brownian noise. The Wavelet Transform is similar to the Fourier transform with a completely different merit function. The main difference between Wavelet transform and Fourier transform is that, in the Wavelet Transform, Wavelets are localized in both time and frequency. In the standard Fourier Transform, Wavelets are only localized in frequency. Wavelet analysis consists of breaking up the signal into shifted and scales versions of the original (or mother) Wavelet. The Wiener Filter (mean squared estimation error) finds implementations as a LMS filter (least mean squares), RLS filter (recursive least squares), or Kalman filter. Quantitative measure (metrics) of the comparison of the denoising algorithms is provided by calculating the Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE) value and the Mean Absolute Error (MAE) evaluation factors. A combination of metrics including the PSNR, MSE, and MAE are often required to clearly assess the model performance

Novel Restoration Techniques for Images Corrupted with High Density Impulsive Noise

Impulse noise is a most common noise which affects the image quality during acquisition or transmission, reception or storage and retrieval process. Impulse noise comes under two categories: (1) fixed-valued impulse noise, also known as salt-and-pepper noise (SPN) due to its appearance, where the noise value may be either the minimum or maximum value of the dynamic gray-scale range of image and (2) random-valued impulse noise (RVIN), where the noisy pixel value is bounded by the range of the dynamic gray-scale of the image. In literature, many efficient filters are proposed to suppress the impulse noise. But their performance is not good under moderate and high noise conditions. Hence, there is sufficient scope to explore and develop efficient filters for suppressing the impulse noise at high noise densities. In the present research work, efforts are made to propose efficient filters that suppress the impulse noise and preserve the edges and fine details of an image in wide range of noise densities. It is clear from the literature that detection followed by filtering achieves better performance than filtering without detection. Hence, the proposed filters in this thesis are based on detection followed by filtering techniques. The filters which are proposed to suppress the SPN in this thesis are: Adaptive Noise Detection and Suppression (ANDS) Filter Robust Estimator based Impulse-Noise Reduction (REIR) Algorithm Impulse Denoising Using Improved Progressive Switching Median Filter (IDPSM) Impulse-Noise Removal by Impulse Classification (IRIC) A Novel Adaptive Switching Filter-I (ASF-I) for Suppression of High Density SPN A Novel Adaptive Switching Filter-II (ASF-II) for Suppression of High Density SPN Impulse Denoising Using Iterative Adaptive Switching Filter (IASF) In the first method, ANDS, neighborhood difference is employed for pixel classification. Controlled by binary image, the noise is filtered by estimating the value of a pixel with an adaptive switching based median filter applied exclusively to neighborhood pixels that are labeled noise-free. The proposed filter performs better in retaining edges and fine details of an image at low-to-medium densities of fixed-valued impulse noise.The REIR method is based on robust statistic technique, where adaptive window is used for pixel classification. The noisy pixel is replaced with Lorentzian estimator or average of the previously processed pixels. Because of adaptive windowing technique, the filter is able to suppress the noise at a density as high as 90%. In the proposed method, IDPSM, the noisy pixel is replaced with median of uncorrupted pixels in an adaptive filtering window. The iterative nature of the filter makes it more efficient in noise detection and adaptive filtering window technique makes it robust enough to preserve edges and fine details of an image in wide range of noise densities. The forth proposed method is IRIC. The noisy pixel is replaced with median of processed pixels in the filtering window. At high noise densities, the median filtering may not be able to reject outliers always. Under such circumstances, the processed left neighboring pixel is considered as the estimated output. The computational complexity of this method is equivalent to that of a median filter having a 3×3 window. The proposed algorithm requires simple physical realization structures. Therefore, this algorithm may be quite useful for online and real-time applications. Two different adaptive switching filters: ASF-I and ASF-II are developed for suppressing SPN at high noise density. The noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Depending on noise estimation, a small filtering window size is initially selected and then the scheme adaptively changes the window size based on the number of noise-free pixels. Therefore, the proposed method removes the noise much more effectively even at noise density as high as 90% and yields high image quality. In the proposed method IASF, noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Due to its iterative structure, the performance of this filter is better than existing order-statistic filters. Further, the adaptive filtering window makes it robust enough to preserve the edges and fine details of an image. Novel Restoration Techniques for Images Corrupted with High Density Impulsive Noise x The filters which are proposed for suppressing random-valued impulse noise (RVIN) are: Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm Adaptive Local Thresholding with MAD (ALT-MAD) Algorithm The proposed method, Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm is a modified MAD (Median of the Absolute Deviations from the median) scheme alongwith a threshold employed for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in adaptive filtering window. Another proposed method for denoising the random-valued and fixed-valued impulse noise is ALT-MAD. A modified MAD based algorithm alongwith a local adaptive threshold is utilized for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in the filtering window of adaptively varied size. Three threshold functions are suggested and employed in this algorithm. Thus, three different versions, namely, ALT-MAD-1, ALT-MAD-2 and ALT-MAD-3 are developed. They are observed to be quite efficient in noise detection and filtering. In the last part of the thesis, some efforts are made to develop filters for color image denoising. The filters which perform better in denoising gray-scale images are developed for suppression of impulsive noise from color images. Since the performance of denoising filters degrades in other color spaces, efforts are made to develop color image denoising filters in RGB color space only in this research work. The developed filters are: Multi-Channel Robust Estimator based Impulse-Noise Reduction (MC-REIR) Algorithm Multi-Channel Impulse-Noise Removal by Impulse Classification (MC-IRIC) Multi-Channel Iterative Adaptive Switching Filter (MC-IASF) Multi-Channel Adaptive Local Thresholding with MAD (MC-ALT-MAD) Algorithm It is observed from the simulation results that the proposed filters perform better than the existing methods. The proposed methods: ASF-1 and IASF exhibit quite superior performance in suppressing SPN in high noise densities compared to other methods. Similarly ALT-MAD-3 exhibits much better performance in suppressing RVIN of low to medium noise densities.The REIR method is based on robust statistic technique, where adaptive window is used for pixel classification. The noisy pixel is replaced with Lorentzian estimator or average of the previously processed pixels. Because of adaptive windowing technique, the filter is able to suppress the noise at a density as high as 90%. In the proposed method, IDPSM, the noisy pixel is replaced with median of uncorrupted pixels in an adaptive filtering window. The iterative nature of the filter makes it more efficient in noise detection and adaptive filtering window technique makes it robust enough to preserve edges and fine details of an image in wide range of noise densities. The forth proposed method is IRIC. The noisy pixel is replaced with median of processed pixels in the filtering window. At high noise densities, the median filtering may not be able to reject outliers always. Under such circumstances, the processed left neighboring pixel is considered as the estimated output. The computational complexity of this method is equivalent to that of a median filter having a 3×3 window. The proposed algorithm requires simple physical realization structures. Therefore, this algorithm may be quite useful for online and real-time applications. Two different adaptive switching filters: ASF-I and ASF-II are developed for suppressing SPN at high noise density. The noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Depending on noise estimation, a small filtering window size is initially selected and then the scheme adaptively changes the window size based on the number of noise-free pixels. Therefore, the proposed method removes the noise much more effectively even at noise density as high as 90% and yields high image quality. In the proposed method IASF, noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Due to its iterative structure, the performance of this filter is better than existing order-statistic filters. Further, the adaptive filtering window makes it robust enough to preserve the edges and fine details of an image. Novel Restoration Techniques for Images Corrupted with High Density Impulsive Noise x The filters which are proposed for suppressing random-valued impulse noise (RVIN) are: Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm Adaptive Local Thresholding with MAD (ALT-MAD) Algorithm The proposed method, Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm is a modified MAD (Median of the Absolute Deviations from the median) scheme alongwith a threshold employed for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in adaptive filtering window. Another proposed method for denoising the random-valued and fixed-valued impulse noise is ALT-MAD. A modified MAD based algorithm alongwith a local adaptive threshold is utilized for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in the filtering window of adaptively varied size. Three threshold functions are suggested and employed in this algorithm. Thus, three different versions, namely, ALT-MAD-1, ALT-MAD-2 and ALT-MAD-3 are developed. They are observed to be quite efficient in noise detection and filtering. In the last part of the thesis, some efforts are made to develop filters for color image denoising. The filters which perform better in denoising gray-scale images are developed for suppression of impulsive noise from color images. Since the performance of denoising filters degrades in other color spaces, efforts are made to develop color image denoising filters in RGB color space only in this research work. The developed filters are: Multi-Channel Robust Estimator based Impulse-Noise Reduction (MC-REIR) Algorithm Multi-Channel Impulse-Noise Removal by Impulse Classification (MC-IRIC) Multi-Channel Iterative Adaptive Switching Filter (MC-IASF) Multi-Channel Adaptive Local Thresholding with MAD (MC-ALT-MAD) Algorithm It is observed from the simulation results that the proposed filters perform better than the existing methods. The proposed methods: ASF-1 and IASF exhibit quite superior performance in suppressing SPN in high noise densities compared to other methods. Similarly ALT-MAD-3 exhibits much better performance in suppressing RVIN of low to medium noise densities.The REIR method is based on robust statistic technique, where adaptive window is used for pixel classification. The noisy pixel is replaced with Lorentzian estimator or average of the previously processed pixels. Because of adaptive windowing technique, the filter is able to suppress the noise at a density as high as 90%. In the proposed method, IDPSM, the noisy pixel is replaced with median of uncorrupted pixels in an adaptive filtering window. The iterative nature of the filter makes it more efficient in noise detection and adaptive filtering window technique makes it robust enough to preserve edges and fine details of an image in wide range of noise densities. The forth proposed method is IRIC. The noisy pixel is replaced with median of processed pixels in the filtering window. At high noise densities, the median filtering may not be able to reject outliers always. Under such circumstances, the processed left neighboring pixel is considered as the estimated output. The computational complexity of this method is equivalent to that of a median filter having a 3×3 window. The proposed algorithm requires simple physical realization structures. Therefore, this algorithm may be quite useful for online and real-time applications. Two different adaptive switching filters: ASF-I and ASF-II are developed for suppressing SPN at high noise density. The noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Depending on noise estimation, a small filtering window size is initially selected and then the scheme adaptively changes the window size based on the number of noise-free pixels. Therefore, the proposed method removes the noise much more effectively even at noise density as high as 90% and yields high image quality. In the proposed method IASF, noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Due to its iterative structure, the performance of this filter is better than existing order-statistic filters. Further, the adaptive filtering window makes it robust enough to preserve the edges and fine details of an image. Novel Restoration Techniques for Images Corrupted with High Density Impulsive Noise x The filters which are proposed for suppressing random-valued impulse noise (RVIN) are: Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm Adaptive Local Thresholding with MAD (ALT-MAD) Algorithm The proposed method, Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm is a modified MAD (Median of the Absolute Deviations from the median) scheme alongwith a threshold employed for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in adaptive filtering window. Another proposed method for denoising the random-valued and fixed-valued impulse noise is ALT-MAD. A modified MAD based algorithm alongwith a local adaptive threshold is utilized for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in the filtering window of adaptively varied size. Three threshold functions are suggested and employed in this algorithm. Thus, three different versions, namely, ALT-MAD-1, ALT-MAD-2 and ALT-MAD-3 are developed. They are observed to be quite efficient in noise detection and filtering. In the last part of the thesis, some efforts are made to develop filters for color image denoising. The filters which perform better in denoising gray-scale images are developed for suppression of impulsive noise from color images. Since the performance of denoising filters degrades in other color spaces, efforts are made to develop color image denoising filters in RGB color space only in this research work. The developed filters are: Multi-Channel Robust Estimator based Impulse-Noise Reduction (MC-REIR) Algorithm Multi-Channel Impulse-Noise Removal by Impulse Classification (MC-IRIC) Multi-Channel Iterative Adaptive Switching Filter (MC-IASF) Multi-Channel Adaptive Local Thresholding with MAD (MC-ALT-MAD) Algorithm It is observed from the simulation results that the proposed filters perform better than the existing methods. The proposed methods: ASF-1 and IASF exhibit quite superior performance in suppressing SPN in high noise densities compared to other methods. Similarly ALT-MAD-3 exhibits much better performance in suppressing RVIN of low to medium noise densities

An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification

This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7

A Low-complexity Wavelet Based Algorithm for Inter-frame Image Prediction

In this paper, a novel multi-resolution variable block size algorithm (MRVBS) is introduced. It is based on: (1) Using the wavelet components of the seven sub-bands from two layers of wavelet pyramid in the lowest resolution; (2) Performing a block matching estimation within a nine-block only in each sub-band of the lower layer; (3) Scaling the estimated motion vectors and using them as a new search center for the finest resolution. The motivation for using the multi-resolution approach is the inherent structure of the wavelet representation. A multi-resolution scheme significantly reduces the searching time, and provides a smooth motion vector field. The approach presented in this paper providing an accurate motion estimate even in the presence of single and mixed noise. As a part of this framework, a comparison of the Full search (FS) algorithm, the three-step search (TSS) algorithm and the new algorithm (MRVBS) is presented. For a small addition in computational complexity over a simple TSS algorithm, the new algorithm achieves good results in the presence of noise

Selected Algorithms of Quantitative Image Analysis for Measurements of Properties Characterizing Interfacial Interactions at High Temperatures.

In the case of every quantitative image analysis system a very important issue is to improve the quality of images to be analyzed, in other words, their pre-processing. As a result of pre-processing, the significant part of the redundant information and disturbances (which could originate from imperfect vision system components) should be removed from the image. Another particularly important problem to be solved is the right choice of image segmentation procedures. Segmentation essence is to divide an image into disjoint subsets that meet certain criteria for homogeneity (e.g. color, brightness or texture). The result of segmentation should allow the most precise determination of geometrical features of objects present in a scene with a minimum of computing effort. The measurement of geometric properties of objects present in the scene is the subject of image analysis