Vernier Image Processing: Model and Experiments

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 110-110
Author(s):  
A V Chihman ◽  
V N Chihman ◽  
Y E Shelepin
Keyword(s):  
Fourier Analysis ◽  
Visual Processing ◽  
Fourier Spectrum ◽  
Field Size ◽  
Two Dimensional ◽  
Frequency Range ◽  
Additional Line ◽  
Point Spread ◽  
Model Predictions ◽  
Spread Function

Earlier we proposed a model for visual processing of the optical image of Vernier targets (1996 Perception25 Supplement, 115 – 116) based on Fourier analysis of the image. Our model comprises blurring of the thin Vernier bars by the optical point-spread function followed by calculation of the two-dimensional Fourier spectrum. In our model the processing area for Fourier analysis (the receptive field size) is 5 min arc. For a Vernier target, the contrast energy in the low-spatial-frequency range is different in different orientations, and magnification of the Vernier shift changes the orientation of the oblique Fourier components. To test the model, we carried out experiments in which the stimuli were Vernier lines with additional line distractors orthogonal to the orientation of the oblique Fourier components. Thresholds for detecting Vernier displacements were determined by a 2AFC paradigm and compared with model predictions. The results are consistent with our modelling of Vernier performance as a measurement of oblique components of the 2-D Fourier spectrum.

Improved Detectability with Blocked Element Compensation

1994 ◽  
Vol 16 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Pai-chi Li ◽  
M. O'Donnell
Keyword(s):  
Cancer Diagnosis ◽  
Performance Measure ◽  
The Body ◽  
Two Dimensional ◽  
Contrast Resolution ◽  
Low Contrast ◽  
Performance Improvements ◽  
Point Spread ◽  
Spread Function ◽  
And Performance

The inability of diagnostic ultrasound to detect low contrast lesions deep inside the body has limited its success in cancer diagnosis. To enhance low contrast detectability, two-dimensional, very large arrays (VLA's) providing greatly improved spatial resolution have been proposed. Due to discontinuous acoustic windows into the body, however, a significant fraction of such an array might be blocked resulting in degraded detectability in clinical situations. To compensate for this degradation, an object dependent method utilizing multiple receive beams has been proposed and shown to effectively reduce un-desired beamforming artifacts. To further explore the method's capabilities, simulations have been done quantifying improvements in contrast resolution. Using the contrast-to-noise ratio (CNR) as a performance measure, results show that low contrast detectability is determined by sidelobe energy in the point spread function if the total aperture size is not reduced. Moreover, contrast resolution can be restored using the object dependent method if the number of blocked elements is not very significant. If the number of blocked elements is large, however, the method breaks down and performance improvements are minimal.

Deconvolution of the two-dimensional point-spread function of area detectors using the maximum-entropy algorithm

1999 ◽  
Vol 32 (4) ◽  
pp. 683-691 ◽  
Author(s):  
H. Graafsma ◽  
R. Y. de Vries
Keyword(s):  
Maximum Entropy ◽  
Point Spread Function ◽  
Contrast Ratio ◽  
Entropy Method ◽  
Protein Crystal ◽  
Two Dimensional ◽  
Two Phase ◽  
X Ray ◽  
Point Spread ◽  
Spread Function

The maximum-entropy method (MEM) has been applied for the deconvolution of the point-spread function (PSF) of two-dimensional X-ray detectors. The method is robust, model and image independent, and only depends on the correct description of the two-dimensional point-spread function and gain factor of the detector. A significant enhancement of both the spatial resolution and the contrast ratio has been obtained for two phase-contrast images recorded with an ultra-high-resolution X-ray imaging detector. The method has also been applied to a Laue diffraction image of a protein crystal, showing an important improvement in both the peak separation of closely spaced diffraction peaks and the signal-to-noise ratio of medium and weak peaks. The principle of the method is explained and examples of its application are presented.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

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