A COMPARATIVE STUDY OF IMAGE FILTERING ON VARIOUS NOISY PIXELS

This paper deals with the comparative study of research work done in the field of Image Filtering. Different noises can affect the image in different ways. Although various solutions are available for denoising them, a detail study of the research is required in order to design a filter which will fulfill the desire aspects along with handling most of the image filtering issues. An output image should be judged on the basis of Image Quality Metrics for ex-: Peak-Signal-to-Noise ratio (PSNR), Mean Squared Error (MSE) and Mean Absolute Error (MAE) and Execution Time

GENETIC FUZZY FILTER BASED ON MAD AND ROAD TO REMOVE MIXED IMPULSE NOISE

In this thesis, a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD) is proposed. The proposed method consists of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization using genetic algorithms (GA) to perform efficient and effective noise removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a pixel. Fuzzy inference system is used to justify the degree of which a pixel can be categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme that adopts median filter as the main estimator is applied to the filtering. The GA training aims to find the best parameters for the fuzzy sets in the fuzzy noise detection. From the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. It also surpasses the other methods, either classical or soft computing-based approaches to impulse noise removal, in MAE and PSNR evaluations. It can also remove salt-and-pepper and uniform impulse noise well

A Novel Image Processing Platform Development For Preprocessing Images With Two-Phase Microstructures Containing Isolated Particles

A novel image processing platform is developed in this thesis for pre-processing the images of two-phase microstructures containing isolated particles based on Matlab®. The two phases in this work represent martensite particles and ferrite matrix in Dual Phase steels that are developed for lightweight automotive body structures. An automated image processing tool is useful for obtaining statistical microstructure characteristics, important for better understanding material\u27s deformation and fracture behavior. However, a generalized commercial software can not automatically realize the microstructural features of special interest, and a user-specified pre-processing tool is needed. This platform utilizes the Matlab® GUI technique, Matlab image processing functions, fuzzy technology to distinguish martensites from ferrites, optimize the intermediate martensite results, analyze and generate the martensite distribution information and volume fraction. The multiple region thresholding technique is proposed to enhance the image segmentation. A fuzzy linguistic system is presented to utilize experts\u27 experience to remove noise and fill the unexpected holes on the image. The examples show that with this platform the pre-processing on images of two phase microstructures containing isolated particles is more convenient, fast and accurate

GENETIC FUZZY FILTER BASED ON MAD AND ROAD TO REMOVE MIXED IMPULSE NOISE

In this thesis, a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD) is proposed. The proposed method consists of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization using genetic algorithms (GA) to perform efficient and effective noise removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a pixel. Fuzzy inference system is used to justify the degree of which a pixel can be categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme that adopts median filter as the main estimator is applied to the filtering. The GA training aims to find the best parameters for the fuzzy sets in the fuzzy noise detection. From the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. It also surpasses the other methods, either classical or soft computing-based approaches to impulse noise removal, in MAE and PSNR evaluations. It can also remove salt-and-pepper and uniform impulse noise well

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

Automatic segmentation of anterior segment optical coherence tomography images

Automatic segmentation of anterior segment optical coherence tomography (AS OCT) images provides an important tool to aid management of ocular diseases. Having precise details about the topography and thickness of an individual eye enables treatments to be tailored to a specific problem. OCT is an imaging technique that can be used to acquire volumetric data of the anterior segment of the human eye. Fast automatic segmentation of this data, which is not available, means clinically useful information can be obtained without the need for time consuming error-prone manual analysis of the images. This thesis presents newly developed automatic segmentation techniques of OCT images. Segmentation of 2D OCT images is first performed. One of the main challenges segmenting 2D OCT images is the presence of regions of the image that generally have a low signal to noise ratio. This is overcome by the use of shape based terms. A number of different methods, such as level set, graph cut, and graph theory, are developed to do this. The segmentation techniques are validated by comparison to expert manual segmentation and previously published segmentation techniques. The best method, graph theory with shape, was able to achieve segmentation comparable to manual segmentation. Good agreement is found with manual segmentation for the best 2D segmentation method, graph theory with shape, achieving a Dice similarity coefficient of 0.96, which is comparable to inter-observer agreement. It performed significantly better than previously published techniques. The 2D segmentation techniques are then extended to 3D segmentation of OCT images. The challenge here is motion artefact or poor alignment between each 2D images comprising the 3D images. Different segmentation strategies are investigated including direct segmentation by level set or graph cut approaches, and segmentation with registration. In particular the latter requires the introduction of a registration step to align multiple 2D images to produce a 3D representation to overcome the presence of involuntary motion artefacts. This method produces the best performance. In particular, it uses graph theory and dynamic programming to segment the anterior and posterior surfaces in individual 2D images with shape constraint. Genetic algorithms are then used to align 2D images to produce a full 3D representation of the anterior segment based on landmarks or geometric constraints. For the 3D segmentation, a data set of 17 eyes is used for validation. These have each been imaged twice so a repeatability measurement can be made. Good repeatability of results is demonstrated with the 3D alignment method. A mean difference of 1.77 pixels is found between the same surfaces of the repeated scans of the same eye. Overall, a new automation method is developed that can produce maps of the anterior and posterior surfaces of the cornea from a 3D images of the anterior segment of a human eye. This will be a valuable tool that can be used for patient specific biomechanical modelling of the human eye