Blur Noise Research Articles

The AAPM/RSNA physics tutorial for residents. Measures of screen-film performance.

The choice of screen-film combination, combined with film-processing conditions, substantially affects radiographic image quality (contrast, blur, and noise) and radiation dose. Film type (single or double emulsion, silver halide content, grain morphology, and spectral sensitivity), processing conditions (chemicals, temperature, time, and agitation), fog level (storage, safelight, light leaks), and characteristics (gradient) determine how the x-ray intensity pattern will be related to the optical density pattern in the radiograph. The type of screen (phosphor layer thickness, light-absorbing dyes and pigments, phosphor particle size), speed of the screen-film processing system (sensitivity), film granularity, screen uniformity, and film contrast affect radiographic noise. Detective quantum efficiency is the basic measure of the efficiency of an imaging system and takes into account the contrast, image blur, speed, and image noise of the system. For radiologists, residents, medical physicists, and technologists involved in medical imaging, it is important to have a basic understanding of the characteristics of screen-film and film-processing systems.

Perceptual characterization of images degraded by blur and noise: model.

Reliable and economic methods for assessing image quality are essential for designing better imaging systems. Although reliable psychophysical methods are available for assessing perceptual image quality with the help of human subjects, the cost of performing such experiments prevents their use for evaluating large amounts of image material. This has led to an increasing demand for objective methods for estimating image quality. The perceived quality of an image is usually determined by several underlying perceptual attributes such as sharpness and noisiness. In the accompanying paper [J. Opt. Soc. Am. A 13, 1166-1177 (1996)] it is demonstrated that the relationships between images on the one hand and judgments on attributes and overall quality by subjects on the other hand can be characterized in a multidimensional perceptual space. In this perceptual space the images are represented by points, and the strengths of their perceptual attributes are modeled by the projections of these image positions onto the attribute axes. In analogy with the perceptual space we will introduce a psychometric space in which the positions of the images are determined by objective measures on the images. In the case of images degraded by blur and noise the stimulus coordinates are functions of the estimated spread of the blurring kernel and the estimated standard deviation of the noise, respectively. According to the model presented in this paper, the perceptual attributes of images can be estimated in three steps. In the first step the physical parameters (blur spread and noise standard deviation) are estimated from the images. In the second step these estimates are used to position the images in psychometric space. In the third step the attribute strengths are derived by projecting the alter image positions onto the attribute axes. We show that the attributes and the quality thus estimated correlate well with the perceived attributes and quality.

Image processing in the digital tomosynthesis for pulmonary imaging

Digital tomosynthesis makes it possible to reconstruct multiple tomographs from digital data obtained during a single tomographic motion and permits digital processing, which adds a number of special advantages to the well-known advantages of conventional tomography. We performed digital tomosynthesis with a fluororadiographic TV unit with tomographic function which was capable of producing pulsed low-and high-energy X-rays alternately, and we studied digital image processing to improve the image clarity of the reconstructed tomographs. To identify the optimal parameters for processing image data by means of spatial frequency filtration we evaluated the spatial frequency distribution of image data in linear tomographs of the lung, and on the basis of the results of this study we developed several types of digital image processing to reduce tomographic blur and system noise, to improve visualisation of faint opacities, to reduce resistant tomographic blur as well as overall blur, and to generate low-noise bone images based on dual-energy subtraction tomosynthesis.

Restoration of differently blurred versions of an image with measurement errors in the PSF's

Restoration of an object from T observations is considered. Each image is distorted by a different deterministic blur and additive noise. The point spread function (PSF) for each observation is unknown; however, a noisy measurement of it is available. Taking the errors in measurements of the PSFs into consideration, the maximum-likelihood and Wiener filters are derived. It is shown that these filters give better results when the regression filter and the conventional Wiener filter, i.e., the one which ignores the presence of the noise in the PSFs. The consistency and the ill-conditioning characteristics of the filters are discussed. Regularized forms for these filers are obtained.

Order statistic-neural network hybrid filters for gamma camera-bremsstrahlung image restoration

An order statistic and neural network hybrid filter (OSNNH) is proposed for the restoration of gamma camera images using the measured modulation transfer function. Planar images of beta-emitting radionuclides are used to evaluate the filter because they exhibit higher degradation than images of single photon emitters due to increased photon scattering and collimator septal penetration. The filter performance is quantitatively evaluated and compared to that of the Wiener filter by investigating the relationship between the externally measured counts from sources of phosphorous-32 ((32)P) at various depths in water. An effective linear attenuation coefficient for (32)P is determined to be equal to 0.13 cm(-1) and 0.14 cm(-1) for the OSNNH and the Wiener filters, respectively. Evaluation of phantom and patient filtered images demonstrates that the OSNNH filter avoids ring effects caused by the ill-conditioned blur matrix and noise overriding caused by matrix inversion, typical of other restoration filters such as the Wiener.

Restoration of images degraded by motion blur using matrix operators

A restoration algorithm is proposed for images degraded by motion blur and additive white noise using matrix operators. The present algorithm is applicable to all noisy motion degraded images. In particular, if the motion blue is a horizontal line motion blue, the Ricatti equation of the present algorithm becomes a scalar-valued equation. Thus, the present algorithm is suitable for image processing from the viewpoint of reduction of memory size and computational time Finally, simulation results of image restoration illustrate the effectiveness of the proposed algorithm.

Lower bound on average mean-square error for image restoration

An average mean-square error bound that is applicable to general image observation models involving degradations of blur, signal-dependent and signal-independent noise, and sensor nonlinearity is derived. A Cramer-Rao lower bound on average mean-square errors for any unbiased image restoration scheme is derived. This bound is analytically expressed as a function of degradation parameters of imaging systems. Potential performance improvements by incorporating signal-dependent noise or sensor nonlinearity into algorithmic design are discussed. >

New results in two-dimensional Kalman filtering with applications to image restoration

This paper describes the application of 2-D Kalman filtering to the restoration of images degraded by linear space invariant (LSI) blur and additive white Gaussian noise (WGN). The image restoration problem is formulated in the framework of the well-known Kalman strip filter. However, the Kalman filtering scheme assumes the availability of a statespace dynamic model for the image process as well as the blur. In the past, most researchers have sought to track the problem of image modeling by making the sometimes unrealistic assumption of separability of the image correlation. A new technique for image modeling which does not make this assumption is proposed. On-line, recursive methods for implementing the modeling algorithm are also presented. We then introduce a novel technique for the state-space realization of separable blurs, since separable 2-D blurs are often encountered in practice and constitute an important subset of 2-D blurs. This state-space model is rendered compatible with strip filtering by using a new recursive scheme which we call as pseudorecursion. An extension for estimating the blur when it is unknown, but can be parameterized, has also been indicated. Simulated experimental results using natural scenery are presented.