Abstract: An imaging system includes a body, a first processing circuit provided at the body, a gimbal carried by the body, an image collection circuit carried by the gimbal, a second processing circuit provided at the gimbal, a third processing circuit provided at the body, and a signal transmission line. One end of the signal transmission line is connected to the second processing circuit, and another end of the signal transmission line is connected to the third processing circuit. The second processing circuit is configured to encode a multi-channel first signal output by the image collection circuit to output a second signal. The third processing circuit is configured to receive the second signal through the signal transmission line, decode the second signal to obtain a decoded second signal, and transmit the decoded second signal to the first processing circuit.

Abstract: A Bayer image detection method and device, and a machine readable storage medium are provided. The method includes: obtaining a Bayer image from an image sensor, the Bayer image including at least one Bayer image unit, the Bayer image unit including four pixels; detecting a distribution mode of two pixels with maximum pixel values among the four pixels in the Bayer image unit; and determining a detection result of the Bayer image according to the distribution mode. In this way, it is unnecessary to detect the entire image in some exemplary embodiments, and it can be determined, based on merely one Bayer image unit, whether the Bayer image has a missing row or missing column, thereby achieving the effect of quickly detecting an error in the Bayer image. An error in the Bayer image can be positioned quickly, which helps improve correction efficiency of the Bayer image.

Abstract: A data processing method includes reading compressed a neural network parameter for a neural network from a memory, decompressing the compressed neural network parameter to generate a decompressed neural network parameter, and processing target data according to the decompressed neural network parameter.

Abstract: An image processing system includes an image sensor including an image output interface, an interface extension device including an extension input interface electrically coupled to the image output interface and at least two extension output interfaces, and at least two image processors coupled to the at least two extension output interfaces respectively. The image sensor is configured to detect an optical signal and convert the optical signal to a raw image signal, and output the raw image signal via the image output interface. The interface extension device is configured to receive the raw image signal from the image output interface via the extension input interface, and output target image signals to be processed corresponding to the raw image signal via the at least two extension output interfaces. The at least two image processors are configured to receive and process the target image signals.

Abstract: An image processing system includes an image sensor configured to obtain raw image data including first-type image data of a first type and second-type image data of a second type, an image data separation device configured to separate the first-type image data from the second-type image data, and an image processor configured to process the first-type image data and the second-type image data that are separated from each other.

Abstract: A method for image processing includes determining an upsample region based on a region excluding a region of interest in an image; and performing an upsampling operation in the upsample region without performing the upsampling operation in the region of interest.

Abstract: An imaging system includes a body, a first processing circuit provided at the body, a gimbal carried by the body, an image collection circuit carried by the gimbal, a second processing circuit provided at the gimbal, a third processing circuit provided at the body, and a signal transmission line. One end of the signal transmission line is connected to the second processing circuit, and another end of the signal transmission line is connected to the third processing circuit. The second processing circuit is configured to encode a multi-channel first signal output by the image collection circuit to output a second signal. The third processing circuit is configured to receive the second signal through the signal transmission line, decode the second signal to obtain a decoded second signal, and transmit the decoded second signal to the first processing circuit.

Abstract: An image processing method is provided for an image device. The method includes obtaining type information of an image sensor of the image device; obtaining multi-path serial image signals outputted by the image sensor; converting the multi-path serial image signals into effective pixels according to the type information; and outputting the effective pixels.

Abstract: System and method can support an image processing device. The image processing device operates to obtain a first set of characterization values, which represents a first group of pixels that are associated with a denoising pixel in an image. Also, the image processing device can obtain a second set of characterization values, which represents a second group of pixels that are associated with a denoising reference pixel. Furthermore, the image processing device operates to use the first set of characterization values and the second set of characterization values to determine a similarity between the denoising pixel and the denoising reference pixel. Then, the image processing device can calculate a denoised value for the denoising pixel based on the determined similarity between the denoising pixel and the denoising reference pixel.

Abstract: System and method can support an image processing device. The image processing device operates to obtain a first set of characterization values, which represents a first group of pixels that are associated with a denoising pixel in an image. Also, the image processing device can obtain a second set of characterization values, which represents a second group of pixels that are associated with a denoising reference pixel. Furthermore, the image processing device operates to use the first set of characterization values and the second set of characterization values to determine a similarity between the denoising pixel and the denoising reference pixel. Then, the image processing device can calculate a denoised value for the denoising pixel based on the determined similarity between the denoising pixel and the denoising reference pixel.

Abstract: System and method can support an image processing device. The image processing device operates to obtain a first set of characterization values, which represents a first group of pixels that are associated with a denoising pixel in an image. Also, the image processing device can obtain a second set of characterization values, which represents a second group of pixels that are associated with a denoising reference pixel. Furthermore, the image processing device operates to use the first set of characterization values and the second set of characterization values to determine a similarity between the denoising pixel and the denoising reference pixel. Then, the image processing device can calculate a denoised value for the denoising pixel based on the determined similarity between the denoising pixel and the denoising reference pixel.

Abstract: System and method can support an image signal processing apparatus, such as a camera system. The image signal processing apparatus includes a processing device, such as a field programmable gate array (FPGA) device, which operates to receive one or more image data from an image sensor via an image data interface. The processing device can forward the received one or more image data to an image signal processor, wherein said image signal processor operates to compress the received one or more image data and save the compressed image data in a first storage medium. Furthermore, the processing device can export the received one or more image data to a second storage medium via a data communication interface.

Abstract: System and method can support an image processing device. The image processing device operates to obtain a first set of characterization values, which represents a first group of pixels that are associated with a denoising pixel in an image. Also, the image processing device can obtain a second set of characterization values, which represents a second group of pixels that are associated with a denoising reference pixel. Furthermore, the image processing device operates to use the first set of characterization values and the second set of characterization values to determine a similarity between the denoising pixel and the denoising reference pixel. Then, the image processing device can calculate a denoised value for the denoising pixel based on the determined similarity between the denoising pixel and the denoising reference pixel.

Abstract: System and method can support an image processing device. The image processing device operates to obtain a first set of characterization values, which represents a first group of pixels that are associated with a denoising pixel in an image. Also, the image processing device can obtain a second set of characterization values, which represents a second group of pixels that are associated with a denoising reference pixel. Furthermore, the image processing device operates to use the first set of characterization values and the second set of characterization values to determine a similarity between the denoising pixel and the denoising reference pixel. Then, the image processing device can calculate a denoised value for the denoising pixel based on the determined similarity between the denoising pixel and the denoising reference pixel.

Abstract: System and method can support an image processing device. The image processing device operates to obtain a first set of characterization values, which represents a first group of pixels that are associated with a denoising pixel in an image. Also, the image processing device can obtain a second set of characterization values, which represents a second group of pixels that are associated with a denoising reference pixel. Furthermore, the image processing device operates to use the first set of characterization values and the second set of characterization values to determine a similarity between the denoising pixel and the denoising reference pixel. Then, the image processing device can calculate a denoised value for the denoising pixel based on the determined similarity between the denoising pixel and the denoising reference pixel.

Abstract: System and method can support an image signal processing apparatus, such as a camera system. The image signal processing apparatus includes a processing device, such as a field programmable gate array (FPGA) device, which operates to receive one or more image data from an image sensor via an image data interface. The processing device can forward the received one or more image data to an image signal processor, wherein said image signal processor operates to compress the received one or more image data and save the compressed image data in a first storage medium. Furthermore, the processing device can export the received one or more image data to a second storage medium via a data communication interface.