A maximum-likelihood method for estimating hyperspectral sensors random noise components, both dependent and independent from the signal, is proposed. A hyperspectral image is locally jointly processed in the spatial and spectral dimensions within a multicomponent scanning window (MSW), as small as 7 × 7 × 7 spatial-spectral pixels. Each MSW is regarded as an additive mixture of spectrally correlated fractal Brownian motion (fBm)-samples and random noise. The main advantage of the proposed method is its ability to accurately estimate band noise variances locally by using spatial and spectral texture correlations from a single textural MSW. For each spectral band, both additive and signal-dependent band noise components are estimated by linear fit of local noise variances obtained from many MSWs distributed over the whole band intensity range. CRLB-based analysis of the estimator performance shows that a good compromise is to jointly process seven adjacent spectral bands. The proposed method performance is assessed first on synthetic fBm-data and on real images with synthesized noise. Finally, four different AVIRIS datasets from 1997 flying season are considered. Good coincidence between additive and signal-dependent AVIRIS random noise components estimates obtained by our method and the estimates retrieved from AVIRIS calibration data is demonstrated. These experiments suggest that it is worth taking into account noise signal-dependency hypothesis for processing AVIRIS data.

Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images

We propose a two-step algorithm that automatically estimates the noise level function of stationary noise from a single image, i.e., the noise variance as a function of the image intensity. First, the image is divided into small square regions and a non-parametric test is applied to decide weather each region is homogeneous or not. Based on Kendall's τ coefficient (a rank-based measure of correlation), this detector has a non-detection rate independent on the unknown distribution of the noise, provided that it is at least spatially uncorrelated. Moreover, we prove on a toy example, that its overall detection error vanishes with respect to the region size as soon as the signal to noise ratio level is non-zero. Once homogeneous regions are detected, the noise level function is estimated as a second order polynomial minimizing the l 1 error on the statistics of these regions. Numerical experiments show the efficiency of the proposed approach in estimating the noise level function, with a relative error under 10% obtained on a large data set. We illustrate the interest of the approach for an image denoising application.

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Estimation of the Noise Level Function Based on a Nonparametric Detection of Homogeneous Image Regions

A comparison of denoising methods for X-ray fluoroscopic images

Purpose
Fluoroscopy is an invaluable tool in various medical practices such as catheterization or image-guided surgery. Patient’s screen for prolonged time requires substantial reduction in X-ray exposure: The limited number of photons generates relevant quantum noise. Denoising is essential to enhance fluoroscopic image quality and can be considerably improved by considering the peculiar noise characteristics. This study presents analytical models of fluoroscopic noise to express the variance of noise as a function of gray level, a practical method to estimate the parameters of the models and a possible application to improve the performance of noise filtering.

X-ray fluoroscopy noise modeling for filter design

Advanced template matching method for estimation of intervertebral kinematics of lumbar spine.

Many established and emerging image processing applications rely on quantum-limited imaging, i.e., imaging in extremely poor illumination. At this, images are corrupted by severe signal-dependent Poisson noise. For optimal noise reduction the noise characteristics must be estimated and integrated into the method. Common noise estimators, however, assume Gaussian noise which is not signal-dependent. In this paper, we describe the modeling process exemplarily for low-dose medical X-ray imaging. In this context, we formulate functional models for detector images and images which have undergone nonlinear white compression prior to further processing. Furthermore, we present a robust estimator for signal-dependent noise suited for real-time applications.

http://www.wseas.us/e-library/conferences/2007corfu/papers/540-210.pdf

Modeling and real-time estimation of signal-dependent noise in quantum-limited imaging

We present a practice-oriented, i.e. fast and robust, estimator for strong signal-dependent noise in medical low-dose X-ray images. Structure estimation by median filtering has shown to be superior to linear binomial filtering. Falsifications due to remaining structure in the estimated noise image are significantly reduced by iterative outlier removal.

Robust and Fast Estimation of Signal-Dependent Noise in Medical X-Ray Image Sequences

An X-ray apparatus for image acquisition and a related method. The apparatus comprises a field-of-view corrector (CS) configured to receive a scout image (SI) acquired by the imager with a tentative collimator setting in a pre-shot imaging phase where said imager operates with a low dosage radiation cone causing the detector to register the scout image. The low dosage cone has, in the detector's image plane, a first cross section smaller than the total area of the detector surface. The field-of-view corrector (CS) uses said scout image to establish field-of-view correction information for a subsequent imaging phase where the imager is to operate with a high dosage radiation cone, the high dosage higher than the low dosage.

X-ray collimator size and position adjustment based on pre-shot

Low-dose X-ray image sequences exhibit severe signal-dependent noise that must be reduced in real-time while, at the same time, preserving diagnostic structures and avoiding artifacts We propose three different methods with applications beyond medical image processing Major contributions are innovative motion detection based on independent binarization of positive and negative temporal differences, real-time multiscale nonlinear diffusion in the presence of severe signal-dependent noise, and multi-resolution inter-scale correlation in shift-dependent pyramids All methods exhibit excellent performance over a broad range of noise, detail, and contrast levels As performance in medical imaging depends to a large degree on the type of intervention and individual preferences of medical staff, no method is generally superior and all methods are considered for the next generation of fluoroscopy systems

Real-Time denoising of medical x-ray image sequences: three entirely different approaches

We present an algorithm for fast automatic registration of spatially overlapping radiographs. It possesses strong robustness against noise, feature masking and feature displacement. Pivotal for this algorithm is an actual interpretation of the stitching feature instead of a simple detection. The proposed method has been evaluated on 3000 clinical radiographs and proved to be a powerful enhancement of established automatic registration algorithms.

/pdf/ruler-based-automatic-stitching-of-spatially-overlapping-3k51ogsrag.pdf

Marc Hensel

Papers

Modeling and real-time estimation of signal-dependent noise in quantum-limited imaging

Robust and Fast Estimation of Signal-Dependent Noise in Medical X-Ray Image Sequences

X-ray collimator size and position adjustment based on pre-shot

Real-Time denoising of medical x-ray image sequences: three entirely different approaches

Ruler-Based Automatic Stitching of Spatially Overlapping Radiographs