Abstract:
The disclosure relates to techniques for calibration of an auto-focus process in an image capture device. The techniques may involve calibration of a lens actuator used to move a lens within a search range during an auto-focus process. For example, an image capture device may adjust reference positions for the search range based on lens positions selected for different focus conditions. The different focus conditions may include a far focus condition and a near focus condition. The focus conditions may be determined based on a detected environment in which the device is used. Detection of an indoor environment may indicate a likelihood of near object focus, while detection of an outdoor environment may indicate a likelihood of far object focus. An image capture device may detect indoor and outdoor environments based on lighting, exposure, or other conditions.
Abstract:
A method and device are provided for mitigating and/or minimizing effects of hand jitter or shaking during an auto-focusing process of an image-capturing device. In order to more accurately focus a lens for capturing a digital image, the lens is moved between a minimum and maximum position (along an axis) while capturing sample image frames using a focus window at various lens positions. Improved auto-focusing is achieved by dynamically adjusting the relative position of the focus window for each image frame to cover substantially the same image portion of the target image. By adjusting the focus window for each image frame to cover substantially the same image portion of the target image, variations from shaking may be minimized, thereby improving auto-focusing.
Abstract:
Techniques are described for predictive focus value calculation within image capture devices. Image capture devices may include digital still cameras and digital video cameras. The techniques include performing an auto-focus process within an image capture device by predicting a focus value for a scene at a lens position of a lens included in the image capture device based on a corrupt focus value for the lens position calculated from a first frame directly after lens settlement. Therefore, the auto-focus process may determine size and direction of movement for the lens to a next lens position based on the predicted valid focus value, and move the lens to the next lens position during a second frame. In this way, the techniques may move the lens to another lens position during each frame, greatly reducing auto-focus latency by potentially doubling or tripling the speed of the auto-focus process.
Abstract:
An imaging system generates a gain for a component of an image format. The gain is at least partially dependent on the brightness of the light source illuminating a scene when an image of the scene was generated. The gain can be used to correct the component of the image format for the color shift in the image caused by the light source. In some instances, the imaging system generates a gain for a plurality of the components of the image format or for all of the components of the image format. The gains can be used to correct the components for the color shift in the image caused by the light source.
Abstract:
A method and apparatus for adaptive green channel odd-even mismatch removal to effectuate the disappearance of artifacts caused by the odd-even mismatch in a demosaic processed image. In one adaptive approach, a calibrated GR channel gain for red rows and a calibrated GB channel gain for blue rows are determined and are a function of valid pixels only in each respective region. After the calibration, in a correction process, the green pixels in red rows of a region are multiplied by the calibrated GR channel gain. On the other hand, the green pixels in blue rows are multiplied by the calibrated GB channel gain. Thus, after demosaic processing, the corrected image has essentially no artifacts caused by odd-even mismatch of the green channel. Alternately, the adaptive green channel odd-even mismatch removal method replaces the center green pixel of a region having an odd number of columns and rows with a normalized weighted green pixel sum total. The weighted green pixel sum total adds the center green pixel weighted by a first weighting factor, a sum of a first tier layer of weighted green pixel values based on a second weighting factor and a sum of a second tier layer of weighted green pixel values based on a third weighting factor.
Abstract:
Apparatus and methods for conditional display of a stereoscopic image pair on a display device are disclosed. Particularly, some implementations include receiving a first image and a second image, determining a vertical disparity between the first image and the second images, and displaying a stereoscopic image pair if the vertical disparity is below a threshold. Some implementations provide for correcting the vertical disparity by generating at least one corrected image, and generating the stereoscopic image pair based on the corrected image. Some implementations may evaluate the quality of the stereoscopic image pair, and display either a two dimensional image or the stereoscopic image pair based on the evaluation.
Abstract:
Described are a system, apparatus, and method to capture a stereoscopic image pair using an imaging device with a single imaging sensor. Particularly, discussed are systems and methods for capturing a first and second image through an image sensor, determining a vertical and horizontal disparity between the two images, and applying corrections for geometric distortion, vertical disparity, and convergence between the two images. Some embodiments contemplate displaying a directional indicator before the second image of the stereoscopic image pair is captured. By displaying a directional indicator, a more optimal position for the second image of the stereoscopic image pair may be found, resulting in a higher quality stereoscopic image pair.
Abstract:
Systems and methods to improve the white balance of a high dynamic range image are disclosed. In a particular embodiment, an imaging device includes a camera sensor and a processor, the processor configured to capture a lighter image and a darker image of a scene. The processor is then configured to white balance the lighter image based on the lighter regions of the image, and to white balance the darker image based on the darker regions of the image. The two images can then be combined to produce a final image.
Abstract:
Apparatus are provided including an image signal carrier, a luminance information evaluator, and a chrominance information modifier. The image signal carrier is encoded with an image signal including luminance information and chrominance information. The luminance information evaluator evaluates the luminance information in the image signal for a given region within the image to identify when the given region is one of substantially white and substantially dark. The chrominance information modifier is provided to modify the chrominance information corresponding to the given region when the given region is one of substantially white and substantially dark
Abstract:
A method and apparatus for adaptive green channel odd-even mismatch removal to effectuate the disappearance of artifacts caused by the odd-even mismatch in a demosaic processed image. In one adaptive approach, a calibrated GR channel gain for red rows and a calibrated GB channel gain for blue rows are determined and are a function of valid pixels only in each respective region. After the calibration, in a correction process, the green pixels in red rows of a region are multiplied by the calibrated GR channel gain. On the other hand, the green pixels in blue rows are multiplied by the calibrated GB channel gain. Thus, after demosaic processing, the corrected image has essentially no artifacts caused by odd-even mismatch of the green channel. Alternately, the adaptive green channel odd-even mismatch removal method replaces the center green pixel of a region having an odd number of columns and rows with a normalized weighted green pixel sum total. The weighted green pixel sum total adds the center green pixel weighted by a first weighting factor, a sum of a first tier layer of weighted green pixel values based on a second weighting factor and a sum of a second tier layer of weighted green pixel values based on a third weighting factor.