Systems and methods are disclosed for image signal processing. For example, methods may include receiving a current image of a sequence of images from an image sensor; combining the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor; determining a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and a set of mixing weights; recirculating the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and storing, displaying, or transmitting an output image that is based on the noise reduced image.

A method for processing a three-dimensional (3D) image includes acquiring a frame of a color image and a frame of a depth image, and generating a frame by combining the acquired frame of the color image with the acquired frame of the depth image. The generating of the frame includes combining a line of the color image with a corresponding line of the depth image.

A method for processing a three-dimensional (3D) image includes acquiring a frame of a color image and a frame of a depth image, and generating a frame by combining the acquired frame of the color image with the acquired frame of the depth image. The generating of the frame includes combining a line of the color image with a corresponding line of the depth image.

A third imaging unit including a pixel not having a polarization characteristic is interposed between a first imaging unit and a second imaging unit including a pixel having a polarization characteristic for each of a plurality of polarization directions. A depth map is generated from a viewpoint of the first imaging unit by matching processing using a first image generated by the first imaging unit and a second image generated by the second imaging unit. A normal map is generated on the basis of a polarization state of the first image. Integration processing of the depth map and the normal map is performed and a depth map with a high accuracy is generated. The depth map generated by the map integrating unit is converted into a map from a viewpoint of the third imaging unit, and an image free from deterioration can be generated.

A third imaging unit including a pixel not having a polarization characteristic is interposed between a first imaging unit and a second imaging unit including a pixel having a polarization characteristic for each of a plurality of polarization directions. A depth map is generated from a viewpoint of the first imaging unit by matching processing using a first image generated by the first imaging unit and a second image generated by the second imaging unit. A normal map is generated on the basis of a polarization state of the first image. Integration processing of the depth map and the normal map is performed and a depth map with a high accuracy is generated. The depth map generated by the map integrating unit is converted into a map from a viewpoint of the third imaging unit, and an image free from deterioration can be generated.

Systems, devices, and techniques related to matching features between a dynamic vision sensor and one or both of a dynamic projector or another dynamic vision sensor are discussed. Such techniques include casting a light pattern with projected features having differing temporal characteristics onto a scene and determining the correspondence(s) based on matching changes in detected luminance and temporal characteristics of the projected features.

Systems, devices, and techniques related to matching features between a dynamic vision sensor and one or both of a dynamic projector or another dynamic vision sensor are discussed. Such techniques include casting a light pattern with projected features having differing temporal characteristics onto a scene and determining the correspondence(s) based on matching changes in detected luminance and temporal characteristics of the projected features.

Described is an imaging component for use by an unmanned aerial vehicle (“UAV”) for object detection. As described, the imaging component includes one or more cameras that are configured to obtain images of a scene using visible light that are converted into a depth map (e.g., stereo image) and one or more other cameras that are configured to form images, or thermograms, of the scene using infrared radiation (“IR”). The depth information and thermal information are combined to form a representation of the scene based on both depth and thermal information.

Described is an imaging component for use by an unmanned aerial vehicle (“UAV”) for object detection. As described, the imaging component includes one or more cameras that are configured to obtain images of a scene using visible light that are converted into a depth map (e.g., stereo image) and one or more other cameras that are configured to form images, or thermograms, of the scene using infrared radiation (“IR”). The depth information and thermal information are combined to form a representation of the scene based on both depth and thermal information.

A dual-camera image capture system may include a first light source, disposed above a target area, a first mobile unit, configured to rotate around the target area, and a second mobile unit, operatively coupled to the first mobile unit, configured to move vertically along the first mobile unit. The dual-camera image capture system may further include a second light source, operatively coupled to the second mobile unit and a dual-camera unit, operatively coupled to the second mobile unit. The dual-camera image capture system may include a first camera configured to capture structural data and a second camera configured to capture color data. The first mobile unit and the second mobile unit may be configured to move the first camera and the second camera to face the target area in a variety of positions around the target area.