Provided is an imaging device that includes an imaging unit that performs an imaging operation, a data generator that generates first power supply voltage data corresponding to a first power supply voltage and a flag generation section that generates a flag signal for the first power supply voltage by comparing the first power supply voltage data and first reference data. The first power supply voltage is supplied to the imaging unit.

A method for image capture includes identifying a bright spot in an image. A neural network is used to recover details in bright spot area through a trained de-noising process. Post-processing of the image is conducted to match image parameters of recovered details in the bright spot area to another area of the image.

Implementations described herein relate to methods, systems, and computer-readable media to relight a video. In some implementations, a computer-implemented method includes receiving a plurality of frames of a video. Each video frame includes depth data and color data for a plurality of pixels. The method further includes segmenting each frame based on the depth data to classify each pixel as a foreground pixel or a background pixel. The method further includes setting depth value of each background pixel to a fixed depth value and applying a Gaussian filter to smooth depth value for the plurality of pixels. The method further includes calculating surface normals based on the depth values of the plurality of pixels. The method further includes rendering a relighted frame by adding a virtual light based on the surface normals and the color data.

Implementations described herein relate to methods, systems, and computer-readable media to relight a video. In some implementations, a computer-implemented method includes receiving a plurality of frames of a video. Each video frame includes depth data and color data for a plurality of pixels. The method further includes segmenting each frame based on the depth data to classify each pixel as a foreground pixel or a background pixel. The method further includes setting depth value of each background pixel to a fixed depth value and applying a Gaussian filter to smooth depth value for the plurality of pixels. The method further includes calculating surface normals based on the depth values of the plurality of pixels. The method further includes rendering a relighted frame by adding a virtual light based on the surface normals and the color data.

An image processing device includes a memory and a processor configured to execute: obtaining a first image captured by an imaging device installed on a mobile object; generating a second image in which brightness of a second area located below a first area of the first image in a vertical direction with respect to a traveling surface of the mobile object, is corrected based on brightness of the first area; and outputting the second image.

Methods, compositions and systems are provided for the imaging of cavity/tissue lesions, including without limitation cavity/tissue malignant lesions, e.g. cancers of the skin, mouth, colon, digestive system cervix, bladder, lung, etc.

Methods, compositions and systems are provided for the imaging of cavity/tissue lesions, including without limitation cavity/tissue malignant lesions, e.g. cancers of the skin, mouth, colon, digestive system cervix, bladder, lung, etc.

Adaptive imaging methods and systems for generating enhanced low light video of an object for medical visualization are disclosed and include acquiring, with an image acquisition assembly, a sequence of reference frames and/or a sequence of low light video frames depicting the object, assessing relative movement between the image acquisition assembly and the object based on at least a portion of the acquired sequence of reference video frames or the acquired sequence of low light video frames, adjusting a level of image processing of the low light video frames based at least in part on the relative movement between the image acquisition assembly and the object, and generating a characteristic low light video output from a quantity of the low light video frames, wherein the quantity of the low light video frames is based on the adjusted level of image processing of the low light video frames.

Adaptive imaging methods and systems for generating enhanced low light video of an object for medical visualization are disclosed and include acquiring, with an image acquisition assembly, a sequence of reference frames and/or a sequence of low light video frames depicting the object, assessing relative movement between the image acquisition assembly and the object based on at least a portion of the acquired sequence of reference video frames or the acquired sequence of low light video frames, adjusting a level of image processing of the low light video frames based at least in part on the relative movement between the image acquisition assembly and the object, and generating a characteristic low light video output from a quantity of the low light video frames, wherein the quantity of the low light video frames is based on the adjusted level of image processing of the low light video frames.

A vehicular camera module includes a main circuit board electrically connected with an imager circuit board via a flexible ribbon cable. Electronic circuitry disposed at a main PCB of the main circuit board includes an image processor. With the imager operated to capture image data, captured image data is provided via the flexible ribbon cable to the electronic circuitry disposed at the main PCB of the main circuit board. The electronic circuitry disposed at the main PCB of the main circuit board includes an electrical connector configured for electrical connection to a connector of a vehicular wire harness. Electrical components disposed at a first side of the main PCB of the main circuit board, electrical components disposed at a second side of the main PCB of the main circuit board, and the electrical connector are electrically operatively coupled together by conductive traces and vias of the main PCB.