9 results about "Depth imaging" patented technology

The application discloses an unsupervised array SAR imaging method with signal-to-noise ratio robustness and target generalization, which is constructed by a deep neural network architecture including two cascaded stages of denoising and reconstruction. In the first stage, the statistical characteristics of the measurement domain are used to remove noise interference, and the pure measurement signal is extracted by using the first stage network. In the second stage, the scene details are recovered by combining the physical forward model and the regularization constraint, and the second stage network is combined with the physical forward projection operator and the regularization constraint. The method overcomes the dependence of the existing deep imaging method on a large amount of high-quality labeled data set, and the defects of high computational complexity and poor noise resistance of the traditional iterative algorithm, and has the characteristics of obtaining high signal-to-noise ratio and high-resolution array SAR images under the condition of only containing noisy observation data, and significantly improving the imaging robustness and generalization ability in complex scenes.

A substrate analysis system includes a load-lock module configured to load or unload a substrate on which a pattern layer is formed; a milling module configured to form a milling surface from which at least a portion of the pattern layer is removed; a depth measuring module configured to measure a milling depth of an analysis region formed on the milling surface; an imaging module configured to capture a two-dimensional image of the analysis region; and a control module controlling the substrate to circulate through the milling module, the depth measuring module, and the imaging module, when the milling depth is shallower than a set target depth, wherein the milling module adjusts a path of the ion beam so that the ion beam moves horizontally in the milling region according to a scanning profile received based on an intensity map of the ion beam.

Embodiments of the present application relate to the field of analyzing materials by optical means, and particularly to a method for determining hyperspectral images of a core half-cylinder. The method provided by the embodiments of the present application calibrates a spectral imaging device and a depth imaging device, and associates the depth values and spectral pixel data obtained by the two imaging devices, so as to determine the accurate transformation relationship between the three-dimensional coordinates obtained by the depth imaging device and the two-dimensional pixel coordinates obtained by the spectral imaging device, to realize spatial registration and fusion of core data collected by different devices, and to facilitate subsequent unified analysis of various core data. The real-time collection of hyperspectral data and depth data of the core half-cylinder by the two imaging devices can provide a reference for core curved surface identification and scanning area positioning, and can also assist in the correction of reflectivity data of the core half-cylinder, reduce the reflectivity calculation error, and help to obtain more accurate hyperspectral images of the core half-cylinder.

This invention discloses a three-dimensional electrical resistivity tomography (EDT) method for detecting cavities based on T-shaped electrode survey lines. A two-dimensional T-shaped electrode survey line advance detection observation system is constructed by deploying two mutually perpendicular survey lines (line 1 and line 2) behind the tunnel face. Electrode electrical resistivity data are collected for both vertical and parallel tunnels, and resistivity depth imaging maps are then established for each. The vertical imaging map determines whether the cavity is located to the left or right of the tunnel face, while the parallel imaging map determines whether it is located in front of or behind the tunnel face. This achieves preliminary two-dimensional planar positioning of the cavity. The detection space around the tunnel face is divided into target units, and through resistivity forward modeling and correlation calculation, correlation imaging results of each target unit are obtained. Ranges exceeding a threshold are delineated in the map, and combined with the preliminary positioning, the three-dimensional spatial location of the cavity is accurately determined, ensuring safe tunnel excavation.

Depth imaging methods and systems with edge and baseline angle correction are disclosed. The system includes an angle-sensitive optical encoder and an image sensor disposed behind the encoder and having orthogonal pixel axes. The system captures image data including two images representing two scene viewpoints separated from each other by an encoder-defined baseline that is obliquely offset relative to a nominal baseline direction parallel to one of the pixel axes. The method can include steps of identifying an edge present in the two images; determining an angle of the edge; determining a parallel disparity, measured along one of the pixel axes, between the edge as viewed in each of the two images; and determining depth information about the edge based on the parallel disparity, the edge angle, and calibration data relating vectorial disparity information to object distance and edge angle information.

The application discloses a plastic embedding agent Big Cut resin, which is composed of a crosslinking agent and a non-reactive liquid auxiliary agent in a mass ratio of 3:7 to 7:3. The application further discloses a preparation method of the plastic embedding agent, which comprises the steps of mixing the crosslinking agent and the non-reactive liquid auxiliary agent in the mass percentage to obtain a mixture, and adding an initiator into the mixture to obtain the Big Cut resin. The application further discloses a plastic embedding method suitable for thick cutting of a large-volume sample, which embeds a biological tissue sample by using the Big Cut resin. The embedding method can realize uniform and stable polymerization of the large-volume resin, improve the cutting performance of the sample after embedding, meet the thick cutting requirement of an fMOST system, has the advantages of transparent depth imaging and adjustable refractive index, and realizes high-throughput imaging.

The application provides a cavity tissue lesion detection device based on a flexible ultrasonic sensor array and a balloon, and belongs to the technical field of medical detection instruments. The device comprises a flexible balloon, a cavity catheter, a layered staggered annular sparse array and a local focusing array. By inflating the balloon, the sensor array is attached to the inner wall, the 16 sensors of the sparse array are distributed in a 360-degree circumferential direction, and the time difference of arrival algorithm is combined to quickly screen and preliminarily locate the lesion; the 4 sensors of the focusing array adopt a compact layout, and the synthetic aperture focusing technology is combined to perform high-resolution depth imaging on the suspicious area. The two different arrays of the application combine the time difference of arrival algorithm and the synthetic aperture focusing technology, realize the accurate dynamic reconstruction of the type and spatial distribution characteristics of the cavity lesion, solve the missed detection risk of the traditional white light endoscope in the screening of the tissue and the narrow field of view of the ultrasonic endoscope, and provide an important quantitative analysis tool for the early identification and treatment of the cavity lesion.

This invention relates to the field of optical microscopy imaging technology, specifically to a large depth-of-focus ultraviolet photoacoustic microscopy imaging system and method, comprising: an ultraviolet photoacoustic signal acquisition module to acquire spatiotemporal distribution data of ultraviolet excitation light and its corresponding photoacoustic response signal data; a spectral-polarization feature extraction module to extract quantitative multidimensional feature parameters related to tissue composition and structure; a cross-depth standard sample module to perform systematic imaging measurements on standard biological tissue samples at different depths to form a feature parameter dataset; and an image reconstruction module that, by linking the spatial distribution characteristics of the ultraviolet excitation light field with the physical propagation laws of photoacoustic signals at different depths, designs a hierarchical signal response adaptation link to match the signal transmission loss differences across tissue depths, and performs signal attenuation correction through a hierarchical dynamic neural radiation field algorithm to reconstruct a tissue microscopic image with large depth-of-focus characteristics. This solves the problems of generally short depths of focus and lack of standardized data support for cross-depth imaging in existing technologies.