A Median Filter Method for Image Noise Variance Estimation

Pei, Zhijun; Qing-qiao, Tong; Wang, Lina; Zhang, Jun

doi:10.1109/itcs.2010.11

Cited by 25 publications

(11 citation statements)

References 7 publications

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“…The two thresholds are toggled in a dynamic manner to maximize Pd and minimize Pja such that; when the PU is predicted to be present the smaller threshold is used and vice versa. The two dynamic thresholds are evaluated based upon the noise uncertainty factor which can be estimated using the noise variance history and the noise variances are estimated as [8]. We prove the effectiveness of our proposed algorithm over the conventional energy detector theoretically and through computer simulations.…”

Section: Dept Of Information and Communication Technologymentioning

confidence: 88%

An efficient dynamic thresholds energy detection technique for Cognitive Radio spectrum sensing

Farag

Ehab

2014

2014 10th International Computer Engineering Conference (ICENCO)

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Cognitive Radio (CR) is an intelligent technique for opportunistic access of idle resources. In CR, Spectrum sensing is one of its important key functionalities. It is used to sense the unused spectrum in an opportunistic manner. Energy detection constitutes a preferred approach for spectrum sensing in cognitive radio due to its simplicity and applicability. The conventional energy detection technique, which is based upon fixed threshold, is sensitive to noise uncertainty which is unavoidable in practical cases. This noise uncertainty gets the fixed threshold energy detector un-optimized in its performance. In this paper, an efficient energy detector is proposed for optimal CR performance. The proposed scheme is based upon a dynamic threshold energy detection algorithm, in which, the decision threshold is toggled between two levels based upon the average energy received from the primary user (PU) during a specified period of observation. Thresholds evaluations are based upon estimating the noise uncertainty factor. These thresholds are used to maximize the probability of detection (Pd) and minimize the probability of false alarm (Pfa). Theoretical analysis and simulation results show the effectiveness of the proposed scheme in comparison to the conventional energy detection method with less increase in complexity.

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Section: Dept Of Information and Communication Technologymentioning

confidence: 88%

An efficient dynamic thresholds energy detection technique for Cognitive Radio spectrum sensing

Farag

Ehab

2014

2014 10th International Computer Engineering Conference (ICENCO)

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“…Medyan filtresi iki boyutlu bir matris içerisindeki bütün değerleri komşu değerlerle karşılaştırarak tarar ve aritmetik sıraya göre ortanca değeri, taranan değerin yeni değeri olarak atar [26].…”

Section: Medyan Filtresiunclassified

Object tracking by PI control and image processing on embedded systems

Öztürk

Albayrak

Polat

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe -Bu çalışmada görüntü işleme ve PI kontrol kullanılarak nesne takip uygulaması gerçekleştirilmiştir. Görüntü işleme tekniği ile tespiti ve takibi istenen nesnenin renk özelliğinden faydalanılmış ve kamera önündeki pozisyonunun sabit kalması hedeflenmiştir. Kontrol uygulaması ise PID kontrol tekniğinin PI varyasyonunu kullanmaktadır. Nesnenin kesin olarak tespit edilmesi için gerekli morfolojik işlemler kullanılmış ve oluşan gürültü, medyan filtresi kullanılarak filtrelenmiştir. Takip edilmesi beklenen nesnenin koordinat bilgisi, kameradan alınan frame içerisinde nesnenin ağırlık merkezinin bulunduğu piksel değeri olarak belirlenmiştir. Nesnenin kameradan alınan görüntü içerisinde merkez noktadan uzaklaşması halinde hata payı, PI kontrol algoritmasının girişine uygulanmaktadır. Tüm bu verilen işlemler tek bir program dâhilinde C programlama dili ile yazılmış ve ARM11 mikroişlemci mimarisine sahip Raspberry Pi geliştirme kartında kullanılmıştır. Görüntü işleme için OpenCV kütüphaneleri kullanılmıştır. Anahtar Kelimler -Nesne Takibi, PI Kontrol, Gömülü sistemAbstract -In this study, object tracking application by using image processing and PI controlling is implemented. The color property of the desired object which was wanted to be detected and tracked by image processing technique was utilized and it's position in front of the camera was aimed to be fixed. Controlling technique is using the PI variation of the PID controlling technique. In order to determine the object definitely morphological applications were used and the noise filtered by the median filter. The coordinate information of the object which was aimed to be tracked was determined as the pixel value which was in the gravity center of the object in the frame received from the camera. If the object within the image received from the camera away from the center point, margin of error is applied to the PI algorithm as a input. All this given procedures were written with C programming under a single program and were used in Raspberry Pi development card which has ARM11 microprocessor architecture. For image processing, OpenCV libraries were used.

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“…Median filtering is particularly useful in suppressing impulsive noise because it removes effectively the noise amplitude outliers [23,24]. Recently, the combination of finite differences and median filtering has been proposed for estimating the image noise in computer vision algorithms [25] where the signal structures of the underlying image need to be removed. The algorithm DIsCOVER proposed in this work is based on the method considered in Pei et al [25].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the combination of finite differences and median filtering has been proposed for estimating the image noise in computer vision algorithms [25] where the signal structures of the underlying image need to be removed. The algorithm DIsCOVER proposed in this work is based on the method considered in Pei et al [25]. For DIsCOVER, a set of finite difference operators (FDOs) are used for suppressing the underlying primary signal structures, leaving a set of noise-only series of points where the MLE of the variance can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks

Martínez¹,

Andrade²

2014

Int J Communication

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SUMMARYBecause of its ease of implementation and minimum requirements about the primary signals' information, energy detection is broadly considered for signal detection in spectrum sensing algorithms. However, the noise uncertainty phenomenon, caused by the random variations in the noise power, degrades the performance of an energy detector, particularly when the signal-to-noise ratio (SNR) is low. In this work, we propose to reduce the negative effects of the noise uncertainty in the performance of an energy detector by dynamically adapting its detection threshold to the noise conditions experienced at each sensing epoch. The noise power is estimated from the received signal samples using an algorithm based on a high-pass filters bank and median filtering. With our proposal, it is possible to maintain a constant and low false alarm rate in the presence of noise uncertainty, without increasing the probability of misdetection, even in the low SNR regime, and without increasing the number of samples considered for spectrum sensing.

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A Median Filter Method for Image Noise Variance Estimation

Cited by 25 publications

References 7 publications

An efficient dynamic thresholds energy detection technique for Cognitive Radio spectrum sensing

An efficient dynamic thresholds energy detection technique for Cognitive Radio spectrum sensing

Object tracking by PI control and image processing on embedded systems

Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks

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