A method for training an asymmetric generative adversarial network to generate an image and an electronic apparatus using the same are provided. The method includes the following. A first real image belonging to a first category, a second real image belonging to a second category and a third real image belonging to a third category are input to an asymmetric generative adversarial network for training the asymmetric generative adversarial network, and the asymmetric generative adversarial network includes a first generator, a second generator, a first discriminator and a second discriminator. A fourth real image belonging to the second category is input to the first generator in the trained asymmetric generative adversarial network to generate a defect image.

Systems and methods for improving soft tissue contrast, characterizing tissue, classifying phenotype, stratifying risk, and performing multi-scale modeling aided by multiple energy or contrast excitation and evaluation are provided. The systems and methods can include single and multi-phase acquisitions and broad and local spectrum imaging to assess atherosclerotic plaque tissues in the vessel wall and perivascular space.

Method and related system (IPS) for visualizing in particular a volume of a substance during its deposition at a region of interest (ROI). A difference image is formed from a projection image and a mask image. The difference image is then analyzed to derive more accurate motion information about a motion or shape of the substance. The method or system (IPS) is capable of operating in an iterative manner. The proposed system and method can be used for processing fluoroscopic X-ray frame acquired by an imaging arrangement (100) during an embolization procedure.

Block-to-block reticle inspection includes acquiring a swath image of a portion of a reticle with a reticle inspection sub-system, identifying a first occurrence of a block in the swatch image and at least a second occurrence of the block in the swath image substantially similar to the first occurrence of the block and determining at least one of a location, one or more geometrical characteristics of the block and a spatial offset between the first occurrence of the block and the at least a second occurrence of the block.

A method of structural masking for progressive health monitoring of a structural component includes receiving a current image of the structural component. A processor aligns the current image and a reference image of the structural component. The processor performs a structure estimation on the current image and the reference image to produce a current structure estimate image and a reference structure estimate image. The processor generates a structural mask from the reference structure estimate image. The processor masks the current structure estimate image with the structural mask to identify one or more health monitoring analysis regions including a potential defect or damaged area appearing in the masked current structure estimate image that does not appear in the reference structure estimate image.

Optical flow refers to the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and a scene. Optical flow algorithms can be used to detect and delineate independently moving objects, even in the presence of camera motion. The present invention uses optical-flow algorithms to detect and remove marine snow particles from live video. Portions of an image scene which are identified as marine snow are reconstructed in a manner intended to reveal underwater scenery which had been occluded by the marine snow. Pixel locations within the regions of marine snow are replaced with new pixel values that are determined based on either historical data for each pixel or a mathematical operation, such as one which uses data from neighboring pixels.

A moving object detection device includes: an image capturing unit with which a vehicle is equipped, and which is configured to obtain a captured image by capturing a view in a travel direction of the vehicle; a calculation unit configured to calculate, for each of first regions which are unit regions of the captured image, a first motion vector indicating movement of an image in the first region; an estimation unit configured to estimate, for each of one or more second regions which are unit regions each including first regions, a second motion vector using first motion vectors, the second motion vector indicating movement of a stationary object which has occurred in the captured image due to the vehicle traveling; and a detection unit configured to detect a moving object present in the travel direction, based on a difference between a first motion vector and a second motion vector.

An apparatus and method for successive multi-frame image denoising are herein disclosed. The apparatus includes a first subtractor including a first input to receive a frame of the image, a second input to receive a reference frame, and an output; an absolute value function block including an input connected to the output of the first subtractor and an output; a second subtractor including a first input connected to the output of the absolute value function block, a second input for receiving a first predetermined value, and an output; and a maximum value divider function block including an input connected to the output of the second subtractor and an output for outputting filter weights.

A sample observation device includes: an emission optical system that emits planar light to a sample on an XZ plane; a scanning unit that scans the sample in a Y-axis direction so as to pass through an emission surface of the planar light; an imaging optical system that has an observation axis inclined with respect to the emission surface and forms an image of observation light generated in the sample; an image acquisition unit that acquires a plurality of pieces of XZ image data corresponding to an optical image of the observation light; and an image generation unit 8 that generates XY image data based on the plurality of pieces of XZ image data. The image generation unit extracts an analysis region of the plurality of pieces of XZ image data acquired in the Y-axis direction, integrates brightness values of at least the analysis region in a Z-axis direction to generate X image data, and combines the X image data in the Y-axis direction to generate the XY image data.

A color conversion apparatus includes a hardware processor that obtains a scanner profile created on the basis of measured RGB values and corresponding measured colorimetric values of patches in a first color chart, and creates a table including correction amounts of RGB values, each according to the level of flare estimated for a patch and each associated with an RGB-value difference and a patch-size difference, on the basis of RGB values of patches in the first color chart and RGB values of patches in a specific chart. The specific chart is created by using a part of the patches in the first color chart with the RGB value or patch size being changed. The hardware processor further corrects measured RGB values of patches in a second color chart with the table, and convers the corrected RGB values into colorimetric values with the scanner profile.