To provide an image processing apparatus, an image processing method, and an endoscope system by which deep tissues and superficial blood vessels enhanced with improved visibility can be displayed to a surgeon in real time. An image processing apparatus according to an embodiment of the present technology includes an enhancement processing unit that performs enhancement processing on low-frequency components that are a range lower than a predetermined spatial frequency in an input image, performs enhancement processing on high-frequency components that are a range higher than the low-frequency components in the input image, and outputs the input image having the low-frequency components and the high-frequency components each subjected to enhancement processing.

Apparatuses, systems, and techniques to generate blue noise masks for real-time image rendering and enhacement. In at least one embodiment, a noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.

A human region detection unit 32 of a mask generation unit 31 detects a target region from a captured image using a region determination result obtained by semantic segmentation, and a difference region detection result. A mask generation processing unit 33 resets a boundary between a target region and a non-target region on the basis of continuity of a pixel value of a captured image, in a boundary re-search region set to include a target region and a non-target region on the basis of a boundary between the target region and the non-target region, such as a background region, that is indicated by a region determination result, and generates a target region mask using the reset boundary. A filtering unit 35 generates an image in which a background region is blurred, by performing filter processing of a region in a captured image that corresponds to a target region mask, using a target region mask generated by the mask generation unit 31, and a blurring filter coefficient set by a filter setting unit 34. It becomes possible to perform background blurring with a small amount of artifact.

A device may receive an image and may process the image, with a first model or a second model, to convert the image into a binary image. The device may generate an identifier that identifies the first model, or identifies the second model and a color removed from the image, and may utilize clustering to cluster pixels of the binary image and to generate a segmented image with a quantity of segments. The device may generate a particular number of segments to select, and may select the particular number of segments, as selected segments, from the quantity of segments. The device may mask the selected segments to generate a protected image with masked segments, and may associate the protected image with the identifier and with original pixel data of the masked segments. The device may store the protected image, the identifier, and the original pixel data in a data structure.

A method of reducing noise in an input image by setting, as a local window among color pixels included in the input image, a target pixel and neighboring pixels adjacent to the target pixel, determining color pixel values for the target pixel and each of the neighboring pixels included in the local window, generating local color average values are generated by averaging, color by color, the color pixel values, generating offset color pixel values by converting the color pixel values of the target pixel and the neighboring pixels based on the local color average values, and generating a compensated color pixel value of the target pixel by adjusting the color pixel value of the target pixel based on the offset color pixel values.

A super resolution is produced using multiple reference images. Reference images are upsampled and blurred as needed for comparison between images of different resolution. Patches in blurred images are searched to find those patches which can be assembled into vectors for improving feature content over multiple resolution levels. The searches are based on similarity maps. The assembled vectors are concatenated with one or more other vectors, up-converted and then passed through convolutional layers to obtain new feature vectors. A final feature vector is passed through a convolutional layer to obtain the super resolution image.

The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

Provided herein are a method of extracting a feature from an image using a laser pattern and an identification device and a robot including the same, and the identification device for extracting a feature from an image using a laser pattern, which includes a first camera coupled to a laser filter and configured to generate a first image including a pattern of a laser which is reflected from an object, a second camera configured to capture an area overlapping an area captured by the first camera to generate a second image, and a controller configured to generate a mask for distinguishing an effective area using the pattern included in the first image and extract a feature from the second image by applying the mask to the second image.

An image processing device includes: a reception unit that receives at least one first image provided from at least one first camera capturing an image of a region in which an object exists and a plurality of second images provided from a plurality of second cameras capturing images of a region including a dead region hidden by the object and invisible from a position of the first camera; and an image processing unit that generates a complementary image, as an image of a mask region in the at least one first image corresponding to the object, from the plurality of second images and generates a synthetic display image by combining the at least one first image and the complementary image.

In accordance with at least some embodiments of the present disclosure, a process to improve computed tomography (CT) to cone beam computed tomography (CBCT) registration is disclosed. The process may include receiving a CT image generated by CT-scanning of an object, and receiving a CBCT image generated by CBCT-scanning of the object. The process may include generating an image mask based on Digital Imaging and Communications in Medicine (DICOM) information extracted from the CBCT image. For a specific pixel in the CBCT image, the image mask contains a corresponding data-field indicating whether the specific pixel contains image data generated based on the CBCT-scanning of the object. The process may further include generating a registered image by utilizing the image mask to perform a DIR between the CT image and the CBCT image.