Implementations disclosed herein include an image capture device, a system, and a method for performing multiscale denoising of a video. An image processor of the image capture device obtains a video frame. The video frame may be in any format and may include noise artifacts. The image processor decomposes the video frame into one or more sub-frames. In some implementations, the image processor denoises each of the one or more sub-frames. The image processor decomposes one or more video frames in a temporal buffer into one or more temporal sub-frames. The image processor denoises each of the temporal sub-frames. The image processor reconstructs the one or more denoised sub-frames and the one or more temporal sub-frames to produce a denoised video frame. A memory of the image capture device may be configured to store the denoised video frame.

Methods and apparatus for generating improved image data from received input image data comprising first input image data associated with a first exposure level and second input image data associated with a second, different, exposure level. Motion detection data is generated from the received input image data by applying a noise model and improved image data is generated by combining data from the first and second input data in dependence on the motion detection data.

A method is provided for detecting mura defects in a master panel during fabrication, the master panel containing multiple flat screen displays. The method includes preparing a combined image from image data of the master panel; enhancing the quality of the combined image, including removing artifacts from the combined image; filtering the enhanced quality combined image to detect local mura defects, the local mura defects including at least one structured pattern of defined geometric shapes; applying different candidate patterns to the filtered combined image; selecting one of the candidate patterns as a defect detection pattern, the defect detection pattern being closest to the structured pattern of defined geometric shapes of the detected local mura defects; and displaying at least a portion of the defect detection pattern on a display, together with the quality-enhanced combined image.

An image processing apparatus detects motion information of an image capturing unit in a time period between capturing of a first image and capturing of a second image by the image capturing unit, and detects a plurality of motion vectors between the first image and the second image. Further, the image processing apparatus determines reliability of the detected motion information, and determines, based on the motion information and the reliability, a motion vector to be used for alignment of the first image and the second image, from the plurality of motion vectors.

A medical picture archive integration system includes a de-identification system that includes a first memory designated for protected health information (PHI), operable to perform a de-identification function is on a DICOM image, received from a medical picture archive system, to identify at least one patient identifier and generate a de-identified medical scan that does not include the at least one patient identifier. The medical picture archive integration system further includes a de-identified image storage system that stores the de-identified medical scan in a second memory that is separate from the first memory, and an annotating system, operable to utilize model parameters received from a central server to perform an inference function on the de-identified medical scan, retrieved from the second memory to generate annotation data for transmission to the medical picture archive system as an annotated DICOM file.

The present disclosure relates to an artificial intelligence (AI) system utilizing a machine learning algorithm, including deep learning and the like, and application thereof. In particular, an electronic device of the present disclosure comprises: a memory including at least one command; and a processor connected to the memory so as to control the electronic device, wherein, by executing the at least one command, the processor acquires an image, acquires a noise correction map for correction of noise of the image on the basis of configuration information of a camera having captured the image or brightness information of the image, and eliminates the noise of the image through the noise correction map. In particular, at least a part of an image processing method may use an artificial intelligence model having been acquired through learning according to at least one of a machine learning algorithm, a neural network algorithm, and a deep learning algorithm.

Systems and methods are disclosed for image capture. For example, methods may include accessing a sequence of images from an image sensor; determining a sequence of parameters for respective images in the sequence of images based on the respective images; storing the sequence of images in a buffer; determining a temporally smoothed parameter for a current image in the sequence of images based on the sequence of parameters, wherein the sequence of parameters includes parameters for images in the sequence of images that were captured after the current image; applying image processing to the current image based on the temporally smoothed parameter to obtain a processed image; and storing, displaying, or transmitting an output image based on the processed image.

An image processing apparatus includes a correction unit configured to correct first image data acquired via an image pickup optical system and to generate second image data using a filter generated based on a characteristic of the image pickup optical system, a decomposition unit configured to decompose each of the first image data and the second image data into a first frequency component and a second frequency component, a combination unit configured to combine the first frequency component of the first image data and the first frequency component of the second image data with each other, and a generation unit configured to generate third image data based on a frequency component including the first frequency component combined by the combination unit and the second frequency component of the second image data.

The present disclosure relates to systems, methods, and computer-readable media to selectively enhance digital image and video content. For example, systems disclosed herein can encode original video content to compress and decompress the original video content. Systems described herein can further identify area of interest information for use in identifying portions of decompressed video content to analyze and remove one or more compression artifacts found therein. Systems described herein can further enhance the decompressed video content by increasing resolution for display. By identifying areas of interest and selectively enhancing digital video content, the systems described herein can reduce consumption of bandwidth and processing resources while maintaining high visual quality of the digital content.

The present invention discloses a plant point cloud acquisition, registration, and optimization method based on a time of flight (TOF) camera, which includes the following steps: (1) placing a to-be-tested plant on a turntable, adjusting a view angle of the TOF camera, and aligning the TOF camera with the to-be-tested plant; (2) turning on the turntable so that it rotates automatically, and enabling the TOF camera to acquire point cloud data of the to-be-tested plant at intervals; (3) performing real-time preprocessing on each frame of point cloud data acquired by the TOF camera; (4) performing registration and optimization on every two adjacent frames of point cloud data, and then integrating the data to obtain complete plant point cloud data; and (5) using statistical filtering to remove the discrete noise in the plant point cloud data obtained in the registration and optimization process to obtain final point cloud data.