A system includes an image sensor, an imaging pipeline, and a display device. The image sensor is configured to capture a first frame of pixel data. The imaging pipeline is coupled to the image sensor to receive the first frame of pixel data. The imaging pipeline includes an adaptive noise filter. The adaptive noise filter is configured to filter a pixel based on noise in the pixel. The imaging pipeline is configured to output a second frame of pixel data. The second frame of pixel data includes pixels filtered by the adaptive noise filter. The display device is coupled to the imaging pipeline to receive the second frame of pixel data. The display device is configured to display the second frame of pixel data.

An automatic clipping technique capable of satisfactorily extracting blood vessels to be extracted is provided. A specific tissue extraction mask image which is created by extracting a specific tissue (for example, a brain) from a three-dimensional image acquired by magnetic resonance angiography and a blood vessel extraction mask image which is created by extracting a blood vessel from an area (a blood vessel search area) which is determined using a preset landmark position and the specific tissue extraction mask image are integrated to create an integrated mask. By applying the integrated mask to the three-dimensional image, a blood vessel is clipped from the three-dimensional image.

The present disclosure relates to image processing. The method includes acquiring at least one of a backward propagation feature of an (x+1)th video frame in a video segment or a forward propagation feature of an (x−1)th video frame in the video segment. The video segment includes N video frames, N being an integer greater than 2, and x being an integer. The method further includes deriving a reconstruction feature of the xth video frame from at least one of the xth video frame, the backward propagation feature of the (x+1)th video frame, or the forward propagation feature of the (x−1)th video frame, and deriving a target video frame corresponding to the xth video frame by reconstructing the xth video frame based on the reconstruction feature of the xth video frame. The target video frame has resolution higher than that of the xth video frame.

A signal processing device and an image display apparatus including the same are disclosed. The signal processing device includes a scaler configured to scale input images of various resolutions to a first resolution, a resolution enhancement processor configured to perform learning on the input images and to output a residual image of the first resolution, and an image output interface configured to output an output image of the first resolution based on a scaling image from the scaler and the residual image from the resolution enhancement processor, and the image output interface changes a weight and an application strength of the residual image according to the area of the input image.

A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

A medical image processing apparatus reducing noise of a medical image acquired includes a smoothing unit forming a smoothed image of the medical image, a route forming unit forming a route that is a pixel group positioned continuously in the smoothed image and fulfills a route condition, and a noise reducing unit extracting a pixel group corresponding to the route in the medical image and reducing noise of the medical image based on the extracted pixel group.

In various examples, high beam control for vehicles may be automated using a deep neural network (DNN) that processes sensor data received from vehicle sensors. The DNN may process the sensor data to output pixel-level semantic segmentation masks in order to differentiate actionable objects (e.g., vehicles with front or back lights lit, bicyclists, or pedestrians) from other objects (e.g., parked vehicles). Resulting segmentation masks output by the DNN(s), when combined with one or more post processing steps, may be used to generate masks for automated high beam on/off activation and/or dimming or shading—thereby providing additional illumination of an environment for the driver while controlling downstream effects of high beam glare for active vehicles.

A method of tracking a CAD model in real time based on a particle filter according to one embodiment of the present disclosure is a method of detecting and tracking a real object based on target object recognition data for a digital model designed on CAD executed by a CAD object tracking detection program installed in a user computing device. The method includes: acquiring an image captured by photographing a surrounding object; detecting a real object corresponding to a shape of a target object designed in CAD from a first frame image of the captured image; and tracking the detected real object in a second frame image of the captured image, wherein the tracking of the detected real object includes determining a new pose of the real object in the second frame image based on the particle filter with respect to an initial pose of the detected real object.

The present disclosure provides systems, methods, and computer program products for controlling an object. An example method can comprise (a) obtaining video data of the object and (b) performing motion analysis on the video data to generate modified video data. The method can further comprise (c) using artificial intelligence (AI) to identify a set of features in the modified video data. The set of features may be indicative of a predicted state of the object. The AI may be been trained offline on historical training data. The method can further comprise (d) using the predicted state to determine a control signal and (e) transmitting, in real-time, the control signal to the object to adjust or maintain a state of the object in relation to the predicted state. Operations (a) to (d) can be performed without contacting the object.

Disclosed is an image processing method, electronic device and storage medium. The method includes obtaining feature information of first region in a current image frame, wherein first region includes a region that is determined by performing motion estimation on the current and previous image frames based on optical flow; obtaining feature information of second region in the current image frame, wherein second region includes a region corresponding to pixel points among first pixel points of the current image frame, where its association with pixel points among second pixel points of the previous image frame satisfies a condition; and based on the feature information of first region and that of second region, fusing the previous and current image frames to obtain a processed current image frame, which is used as a previous image frame for a next image frame.