Chapter 15: Estimation of Noise in Gray-Scale and Colored Images Using Median Absolute Deviation (MAD)

Khalil, Hassan H.; Rahmat, Rahmita O. K.; Mahmoud, Waleed A.

doi:10.1109/gmai.2008.7

Cited by 18 publications

(7 citation statements)

References 12 publications

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“…In this part, four classical noise level estimation methods and three state-of-the-art noise level estimation methods are introduced in the comparison experiments to evaluate the performance of the proposed AWGN level estimation method. The Immerkær’s fast noise variance estimation (FNVE) [9], Khalil’s median absolute deviation (MAD) based noise estimation [39], Santiago’s variance mode (VarMode) noise level estimation [40] and Zoran’s discrete cosine transform (DCT) based noise estimation [12] were chosen as the classical noise level estimation methods. The Olivier’s nonlinear noise estimator (NOLSE) [11], Pyatykh’s principal component analysis (PPCA) based noise esti-mation [17] and Lyu’s noise variance estimation (EstV) [13] were selected as the state-of-the-art noise estimation methods.…”

Section: Resultsmentioning

confidence: 99%

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Wei

et al. 2019

Sensors

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Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

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Section: Resultsmentioning

confidence: 99%

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Wei

et al. 2019

Sensors

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“…In this method, the value of a data point is replaced by that of the median of all data points in a neighborhood w [ 14 ], such that: y[m,n] = median {x[i,j], (i,j) ϵ w} (ii) Median absolute deviation (MAD): This is calculated by taking the absolute difference between each point and the median, and then calculating the median of those differences. This is more robust than using the standard deviation for outlier detection as the standard deviation is itself affected by the presence of outliers [ 15 ]. (d) Inter-quartile range(IQR): The concepts of using the Z-score and inter-quartile range (IQR) to study outliers were discussed in our previous study [ 1 ].…”

Section: Experimental Methodology and Data Explorationmentioning

confidence: 99%

MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

Kar

2020

Vision

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Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In previous research on pattern analysis of gaze data, efforts were made to model human visual behaviors and cognitive processes. What remains relatively unexplored are questions related to identifying gaze error sources as well as quantifying and modeling their impacts on the data quality of eye trackers. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.

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“…The median absolute deviation counters the idea of the mean absolute deviation in estimating noise [23]. The median absolute deviation is more robust than the mean absolute deviation and is less affected by outliers, which makes the edges in non-smooth regions less affected by the overall score.…”

Section: Noise Parameter Estimationmentioning

confidence: 99%

A Study on Distortion Estimation Based on Image Gradients

Chin¹,

Chow²,

Kanesan³

et al. 2022

Sensors

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Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.

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Chapter 15: Estimation of Noise in Gray-Scale and Colored Images Using Median Absolute Deviation (MAD)

Cited by 18 publications

References 12 publications

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

A Study on Distortion Estimation Based on Image Gradients

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