Images Of Rough Surfaces Research Articles

Imaging of Rough Surfaces by RTM Method

Imaging of Rough Surfaces by RTM Method

Imaging of Rough Surfaces by Near-field Measurement

Imaging of Rough Surfaces by Near-field Measurement

Multilayer Brightness Temperature Tracing Method for Rough Surface Scene Simulation in Passive Millimeter-Wave Imaging

Simulation in passive millimeter-wave (MMW) imaging of rough surfaces is an indispensable step in the simulation in passive radiation imaging, especially for the rough surfaces of different roughness surfaces. However, little attention has been paid to the simulation of rough surface; based on the existing model of brightness temperature tracing described in previous work, diffused reflection of the rough surface is taken into account in the improved model which is presented in this paper. In the paper, the brightness temperature tracing model of different roughness surfaces has been established. Then, we present a method called multilayer brightness temperature tracing (MBTT) method to obtain the radiation brightness temperature of rough surface. Hence, the discrimination of brightness temperature tracing method is enhanced.

Imaging of Rough Surfaces Having Impedance Boundary Condition by the Use of Newton Iterative Algorithm

In this letter, an inverse scattering problem for the reconstruction of rough surfaces having standard impedance boundary condition (IBC) is presented. The method presented in this letter is the extension of a method given by Yapar to the case of IBC. The reconstruction is obtained from the scattered field measurements for a plane wave illumination of the inaccessible rough surface. Through a special representation of the scattered field in terms of a Fourier transformation and a Taylor series expansion, the problem is first reduced to a nonlinear operator equation. Then, this nonlinear equation is iteratively solved via the Newton method. Numerical results show that the method produces quite satisfactory results regardless of the values of the impedance.

A discrete-scatterer model for ultrasonic images of rough surfaces

Automated analysis of ultrasonic images could be greatly improved with model-based Bayesian methods for image analysis. Such an approach would require an accurate probabilistic image model representing ultrasonic images in terms of the gross shape of underlying anatomical structure. Existing probabilistic models for ultrasonic image data do not adequately incorporate structure shape or system characteristics; thus, a substantially new approach is warranted. Toward that goal, we have developed models for the imaging system and rough surface with the following objectives: (1) accuracy in representation of basic image characteristics such as the texture and intensity, (2) a minimum of computational requirements, and (3) a form that is naturally extendable to an appropriate probabilistic image model. The imaging system was modeled as a linear system with a separable three-dimensional point-spread function with an envelope of Gaussian curves in each dimension. The rough surface was modeled as a collection of discrete scatterers placed on the continuum and parametrized by a surface roughness and scatterer concentration. Models were evaluated by a visual comparison of actual and simulated images of a cadaveric lumbar vertebra. The gross shape of the vertebral surface was estimated from computed tomography images of the vertebra, and simulated images were generated using the models and the gross surface shape. Actual images were registered with the surface and simulated images to within 2 mm. The similarity of the actual and simulated images was quite remarkable considering the simplicity of the models. Differences between the images were less than those between two simulated images separated by 0.4 mm or one-fifth the registration error. Further assessment of the models would require a statistical approach not yet available. The models do, however, provide the basis for the development of a computationally tractable probabilistic image model for image analysis. Such a model will provide the means for a statistical evaluation of the system and surface models.

Imaging and Reconstruction for Rough Surface Scattering in the Kirchhoff Approximation by Confocal Microscopy

Abstract Imaging of rough surfaces in confocal microscopy can be described using the concept of the three-dimensional (3D) coherent transfer function (CTF), which is developed on the basis of the Kirchhoff approximation. The 3D CTF is presented using a scalar but high-angle theory. Methods for reconstruction of surface profiles are considered.

Tip artifacts of microfabricated force sensors for atomic force microscopy

It is demonstrated that due to inevitable intrinsic imperfections in the microfabrication process of atomic force microscopy (AFM) tips, images of rough surfaces can be totally dominated by tip artifacts. These images reflect the mesoscopic tip shape as concluded from a comparison of AFM and scanning electron microscopy images of the tip and sample. These tip artifacts have been found on a scale of 20–600 nm, showing the necessity of characterizing the tip shape in order to make reliable sample-specific statements.

Evidence for tip imaging in scanning tunneling microscopy

It is demonstrated that scanning tunneling microscopy (STM) images often contain three-dimensional ghost images of the tunneling tip. These ghost images directly reflect the shape of the tip, as is proven by comparing them with tip indentations made in Si. Tip images appear as a set of identical protrusions, and have been observed regularly on Si surfaces annealed at 1200 K in ultrahigh vacuum. Imaging of rough surfaces may be fully dominated by this effect which can lead to incorrect image interpretations in STM and AFM.