Abstract:
A method and apparatus performing scene-adaptive auto-focusing for image capture with a variable-focus lens. The array of color pixels includes a array of half-covered light sensors to obtain lens-focus state information. The exposure time of the plurality of partially-covered light sensors is dynamically selected as long-exposure or short-exposure, based upon a current measurement of a property (e.g., brightness, or color-specific brightness) of a selected region of interest within a scene to be captured by the array. Then, focus state information corresponding to the selected region of interest is obtained by capturing light from the selected region of interest with first and second partially-covered light sensors. The exposure time of the partially-covered light sensors can be changed based on whether the brightness is greater than a predetermined threshold value.
Abstract:
An image processing method includes: receiving image data of a Bayer format comprising red color information, green color information, and blue color information, generating image data of a modified Bayer format by combining the green color information with the red color information and combining the green color information with the blue color information while downscaling the image data of the Bayer format to a target resolution, denoising the image data of the modified Bayer format, and generating RGB image data by demosaicing the denoised image data of the modified Bayer format.
Abstract:
Exemplary embodiments of the invention as described herein generally provide for detecting the displacement of feature(s) within a visual image in cases where pattern matching fails due to the existence of aperture(s) caused for example by external condition(s) encountered in recording such an image over time. Technique(s) are disclosed for detecting the difference between displacement of a geometric feature of an object appearing within an image (e.g., an edge or smooth surface) that has an aperture and another feature (e.g., a corner) that does not since it is not symmetrically invariant.
Abstract:
An image processing method includes: receiving image data of a Bayer format comprising red color information, green color information, and blue color information, generating image data of a modified Bayer format by combining the green color information with the red color information and combining the green color information with the blue color information while downscaling the image data of the Bayer format to a target resolution, denoising the image data of the modified Bayer format, and generating RGB image data by demosaicing the denoised image data of the modified Bayer format.
Abstract:
Systems and methods for setting the white balance of an image are described. Embodiments of the systems and methods may receive image data comprising a plurality of exposures, generate a plurality of white balance values based on merge information from a high dynamic range (HDR) merge of the exposures, and adjust a white balance of each pixel of the image data based on the white balance values.
Abstract:
A method and apparatus performing scene-adaptive auto-focusing for image capture with a variable-focus lens. The array of color pixels includes a array of half-covered light sensors to obtain lens-focus state information. The exposure time of the plurality of partially-covered light sensors is dynamically selected as long-exposure or short-exposure, based upon a current measurement of a property (e.g., brightness, or color-specific brightness) of a selected region of interest within a scene to be captured by the array. Then, focus state information corresponding to the selected region of interest is obtained by capturing light from the selected region of interest with first and second partially-covered light sensors. The exposure time of the partially-covered light sensors can be changed based on whether the brightness is greater than a predetermined threshold value.
Abstract:
The present disclosure describes compression of high dynamic range (HDR) images into low dynamic range (LDR) images while saving useful data in the HDR image. HDR images can be formed by merging images with different exposure times into a single image with high bit-depth to capture low light and bright data. LDR images can be generated using a joint auto-exposure and tone mapping system to capture useful details of the HDR input in and LDR output. Therefore, embodiments of the present disclosure create high quality LDR images from HDR images using data from the auto-exposure system of a sensor.
Abstract:
Systems and methods for setting the white balance of an image are described. Embodiments of the systems and methods may receive image data comprising a plurality of exposures, generate a plurality of white balance values based on merge information from a high dynamic range (HDR) merge of the exposures, and adjust a white balance of each pixel of the image data based on the white balance values.
Abstract:
The present disclosure describes compression of high dynamic range (HDR) images into low dynamic range (LDR) images while saving useful data in the HDR image. HDR images can be formed by merging images with different exposure times into a single image with high bit-depth to capture low light and bright data. LDR images can be generated using a joint auto-exposure and tone mapping system to capture useful details of the HDR input in and LDR output. Therefore, embodiments of the present disclosure create high quality LDR images from HDR images using data from the auto-exposure system of a sensor.
Abstract:
A method of rectifying stereo images includes providing a plurality of pairs of sets of keypoints extracted from a pair of current stereo images and from a pair of previous stereo images wherein each pair of stereo images includes a left image and a right image respectively obtained from a left camera and a right camera; providing a plurality of pairs of sets of next-choice-match points extracted from the pair of current stereo images and the pair of previous stereo images; finding one or more anchor points in a left previous image; finding a right linking point which is the corresponding keypoint in the right previous image, and a left linking point which is the corresponding keypoint in the left current image; finding a closing point; and calculating a cost from the right linking point, the left linking point, and the closing point.