A non-transitory computer-readable medium storing computer program code executed by a computer processor that executes an imaging process comprising: acquiring a medical image generated based on a signal detected by a catheter insertable into a body lumen; estimating a cause of an image defect by inputting the acquired medical image to a model learned to output the cause of the image defect when the medical image in which the image defect occurs is input; and outputting introduction information for introducing a countermeasure for removing the estimated cause of the image defect.

Provided by the present disclosure are a method and device for removing handwritten content from a text image, and a storage medium. The method for removing handwritten content from a text image comprises: acquiring an input image of a text page to be processed, wherein the input image comprises a handwritten area, and the handwritten area comprises the handwritten content; using an image segmentation model to recognize the input image so as to obtain an initial handwritten pixel of the handwritten content; performing blurring processing on the initial handwritten pixel to obtain a handwritten pixel mask area; determining the handwritten content according to the handwritten pixel mask area; and removing the handwritten content from the input image to obtain an output image.

An image processing method includes: determining a first face mask image that does not contain hair from a target image, and obtaining a first face region that does not contain hair from the target image according to the first face mask image; filling a preset grayscale color outside the first face region to generate an image to be sampled; performing down-sampling on the image to be sampled to obtain sampling results, and obtaining remaining sampling results by removing one or more sampling results in which a color is the preset grayscale color from the sampling results; obtaining a target color by calculating a mean color value of the remaining sampling results and performing weighted summation on a preset standard face color and the mean color value; rendering pixels in a face region of the target image according to the target color.

A picture processing method includes: identifying a target area where a QR code is located in a picture; obtaining parameters of the picture; and performing effacing process on the target area of the picture based on the parameters.

A mixed reality (MR) providing device is disclosed. The MR providing device includes: a camera, a communication unit comprising circuitry configured to communicate with an electronic device providing video, an optical display unit comprising a display configured to simultaneously display real space within a preset range of viewing angle and a virtual image, and a processor. The processor is configured to: capture the preset range of viewing angle through the camera to acquire an image, identify at least one semantic anchor spot of the acquired image in which an object may be positioned, transmit characteristic information of the semantic anchor spot related to the object that may be positioned to the electronic device through the communication unit, receive an object region including the object corresponding to the characteristic information and included in an image frame of the video from the electronic device through the communication unit, and control the optical display unit to display the received object region on the semantic anchor spot.

An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.

In variants, the method for occlusion correction can include: determining a measurement depicting an occluded object of interest (OOI), optionally infilling the occluded portion of the object of interest within the measurement, and determining an attribute of the object of interest based on the infilled measurement.

Media processing systems and techniques are described. A media processing system receives image data that represents an environment captured by an image sensor. The media processing system receives an indication of an object in the environment that is represented in the image data. The media processing system divides the image data into regions, including a first region and a second region. The object is represented in one of the plurality of regions. The media processing system modifies the image data to obscure the first region without obscuring the second region based on the object being represented in the one of the plurality of regions. The media processing system outputs the image data after modifying the image data. In some examples, the object is depicted in the first region and not the second region. In some examples, the object is depicted in the second region and not the first region.

The present invention discloses an intelligent cloud-based photo enhancement system to improve the quality and aesthetics of an image captured via an electronic device. The cloud-based photo enhancement system comprises a cloud database containing a collection of high-quality photos taken at various locations worldwide and under different environment settings. Information from photos stored in the cloud database taken using better photographic capability cameras is used to improve details that the new photo fails to capture. The system relies not only on machine learning models but also utilizes data associated with the new image collected from the hardware of the electronic device such as camera settings, GPS and phone sensors, which results in more reliable information.