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

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

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

Fuzzy metrics and fuzzy logic for colour image filtering

El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido. En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente. En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879Palanci

Image information restoration based on long-range correlation

2001-2002 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Optimum Median Filter Based on Crow Optimization Algorithm

يُقترح مرشح متوسط ​​جديد يعتمد على خوارزميات تحسين الغراب (OMF) لتقليل ضوضاء الملح والفلفل العشوائية وتحسين جودة الصور ذات اللون الرمادي والملونة . الفكرة الرئيسية لهذا النهج هي أن أولاً ، تقوم خوارزمية تحسين الأداء بالكشف عن وحدات البكسل الخاصة بالضوضاء ، واستبدالها بقيمة وسيطة مثالية تبعًا لدالة الأداء. أخيرًا ، تم استخدام نسبة القياس القصوى لنسبة الإشارة إلى الضوضاء (PSNR) ، والتشابه الهيكلي والخطأ المربع المطلق والخطأ التربيعي المتوسط ​​لاختبار أداء المرشحات المقترحة (المرشح الوسيط الأصلي والمحسّن) المستخدمة في الكشف عن الضوضاء وإزالتها من الصور. يحقق المحاكاة استنادًا إلى MATLAB R2019b والنتائج الحالية التي تفيد بأن المرشح المتوسط ​​المحسّن مع خوارزمية تحسين الغراب أكثر فعالية من خوارزمية المرشح المتوسط ​​الأصلية ومرشحات لطرق حديثة ؛ أنها تبين أن العملية المقترحة قوية للحد من مشكلة الخطأ وإزالة الضوضاء بسبب مرشح عامل التصفية المتوسط ​​؛ ستظهر النتائج عن طريق تقليل الخطأ التربيعي المتوسط ​​إلى أدنى أو أقل من (1.5) ، والخطأ المطلق للتساوي (0.22) ,والتشابه الهيكلي اكثر من ( 95%) والحصول على PSNR أكثر من 45dB).) وبنسبة تحسين ( 25%) .          A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%)