A dual sensor imaging system and a depth map calculation method thereof are provided. The dual sensor imaging system includes at least one color sensor, at least one infrared ray (IR) sensor, a storage device, and a processor. The processor is configured to load and execute a computer program stored in the storage device to: control the color sensor and the IR sensor to respectively capture multiple color images and multiple IR images by adopting multiple exposure conditions suitable for an imaging scene, adaptively select a combination of the color image and the IR image that are comparable to each other from the color images and the IR images; and calculate a depth map of the imaging scene by using the selected color image and IR image.

A method includes obtaining multiple input image frames and determining how to warp at least one of the input image frames. The method also includes performing an intraband demosaic-warp operation to reconstruct image data in different color channels of the input image frames and warp the at least one input image frame to produce RGB input image frames. The method further includes blending the RGB input image frames to produce a blended RGB image frame, performing an interband denoising operation to produce a denoised RGB image frame, and performing an interband sharpening operation to produce a sharpened RGB image frame. In addition, the method includes performing an interband demosaic operation to substantially equalize high-frequency content in different color channels of the sharpened RGB image frame to produce an equalized sharpened RGB image frame and generating a final image of the scene based on the equalized sharpened RGB image frame.

There is provided a method for capturing a sequence of image frames in a thermal camera having a microbolometer detector comprising: capturing a first sequence and a second sequence of image frames with a shutter of the thermal camera being in a closed state and an open state, respectively. While capturing each of the first and the second sequence, an integration time of the microbolometer detector is switched between a plurality of integration times according to one or more repetitions of a temporal pattern of integration times. The method further comprises correcting image frames in the second sequence that are captured when the integration time is switched to a particular position within the temporal pattern of integration times using image frames in the first sequence that are captured when the integration time is switched to the same particular position within the temporal pattern of integration times.

The present disclosure provides a multi-exposure image fusion (MEF) method based on a feature distribution weight of a multi-exposure image, including: performing color space transformation (CST) on an image, determining a luminance distribution weight of the image, determining an exposure distribution weight of the image, determining a local gradient weight of the image, determining a final weight, and determining a fused image. The present disclosure combines the luminance distribution weight of the image, the exposure distribution weight of the image and the local gradient weight of the image to obtain the final weight, and fuses the input image and the weight with the existing pyramid-based multi-resolution fusion method to obtain the fused image, thereby solving the technical problem that an existing MEF method does not consider the overall feature distribution of the multi-exposure image.

A smart camera for an electronic monitoring system is provided. The smart camera includes an imaging device having a field of view and being configured to capture an image. The imaging device has a variable exposure. A detector is operatively connected to the imaging device and configured to generate a trigger signal in response to activity within the field of view. A first processor is operatively connected to the detector and the imaging device, and a second processor is operatively connected to the detector and the imaging device. A computer-readable memory is operatively connected to the first processor and the secondary processor. The computer-readable memory is configured to store a map of ambient light levels in the field of view at selected times of day. The second processor varies the exposure of the imaging device in response to the trigger signal.

A space debris observation method based on alternating exposure times of a charge coupled device (CCD) camera is provided. The present disclosure controls the CCD camera to acquire consecutively and alternately short-exposure and long-exposure images based on preset exposure times. The present disclosure realizes detection and astronomical positioning of low-orbit, medium-orbit and high-orbit space debris by processing short-exposure images of odd-numbered frames. The present disclosure realizes detection of medium-orbit and high-orbit space debris by processing long-exposure images of even-numbered frames, and realizes astronomical positioning of the medium-orbit and high-orbit space debris detected in a current frame through plate constant model coefficients of adjacent odd-numbered frames. In addition, in a search mode, the present disclosure realizes precise astronomical positioning of low-orbit, medium-orbit and high-orbit space debris through a multi-point adjustment method and the plate constant model coefficients of adjacent odd-numbered frames.

Embodiments of the present invention are an image exposure method and device for an unmanned aerial vehicle, and an unmanned aerial vehicle. The method comprises: firstly, acquiring the original image information about a target object, then obtaining the weighted image information according to the original image information, further obtaining the compensation amount of an automatic exposure according to the weighted image information, and finally adjusting an automatic exposure strategy according to the compensation amount of the automatic exposure. The method prevents an unmanned aerial vehicle from easily losing a target during the process of the unmanned aerial vehicle automatically following a moving object, even when encountering a change in light and shadow.

An apparatus includes at least one memory configured to store an AI network and at least one processor. The at least one processor is configured to generate a dead leaves model. The at least one processor is also configured to capture a ground truth frame from the dead leaves. The at least one processor is further configured to apply a mathematical noise model to the ground truth frame to produce a noisy frame. In addition, the at least one processor is configured to train the AI network using the ground truth frame and the noisy frame.

An image capturing device with a sensor, a memory, and a processor is provided. The sensor captures an image of at least one target. The memory stores a plurality of instructions. The processor obtains the plurality of instructions to perform the following steps: controlling the sensor to capture a reference image and a processed image; capturing a first bright region and a dark region from the reference image, and capturing a second bright region from the processed image; performing calculations on a first brightness value of the first bright region and a second brightness value of the second bright region respectively with at least two first brightness thresholds, to obtain a first low exposure compensation value and a second low exposure compensation value; and obtaining a high exposure compensation value according to comparisons between a third brightness value of the dark region and at least two second brightness thresholds.

Aspects of the present disclosure relate to systems and methods for generating a composite image. This can include a western blot imager with a real time camera. One aspect of the present disclosure relates to an imaging system. The imaging system includes a sample plane that can receive and hold a sample, a photon resolving camera, and a lens attached to the photon resolving camera, the photon resolving camera and the lens positioned to image the sample plane.