Abstract: An example method of single-exposure multiple-frame image capture, making an HDR video, comprising, capturing a series of frames at an underexposed setting, selecting a reference image from the series of frames, aligning the series of frames to the reference image, merging the series of frames into one frame based on the reference image, indexing the reference image to a next frame in the series of frames, resulting in an indexed reference image, realigning the series of frames to the indexed reference image and remerging the series of frames into a single frame based on the indexed reference image.

Abstract: A method of auto white balancing, including, receiving an original image, determining an RG logarithmic ratio of a set of red to green channel values of the original image, determining a BG logarithmic ratio of a set of blue to green channel values of the original image, determine an original two-dimensional histogram utilizing the RG logarithmic ratio and the BG logarithmic ratio, determine a Gaussian-blur two-dimensional histogram utilizing the RG logarithmic ratio and the BG logarithmic ratio, determining a sharpened two-dimensional histogram of a sharpened image utilizing the RG logarithmic ratio and the BG logarithmic ratio, determining a Laplacian-edge two-dimensional histogram of a Laplacian-edge image utilizing the RG logarithmic ratio and the BG logarithmic ratio and determining a white balancing gain utilizing a neural network based on the original 2D histogram, the Gaussian-blur 2D histogram, the sharpened 2D histogram and the Laplacian-edge 2D histogram.

Abstract: A method of auto white balancing, including, receiving an original image, determining an RG logarithmic ratio of a set of red to green channel values of the original image, determining a BG logarithmic ratio of a set of blue to green channel values of the original image, determine an original two-dimensional histogram utilizing the RG logarithmic ratio and the BG logarithmic ratio, determine a Gaussian-blur two-dimensional histogram utilizing the RG logarithmic ratio and the BG logarithmic ratio, determining a sharpened two-dimensional histogram of a sharpened image utilizing the RG logarithmic ratio and the BG logarithmic ratio, determining a Laplacian-edge two-dimensional histogram of a Laplacian-edge image utilizing the RG logarithmic ratio and the BG logarithmic ratio and determining a white balancing gain utilizing a neural network based on the original 2D histogram, the Gaussian-blur 2D histogram, the sharpened 2D histogram and the Laplacian-edge 2D histogram.

Abstract: A method of defect pixel correction, includes, receiving at least one center pixel signal in a plurality of frames, receiving a plurality of neighboring pixel signals adjacent the center pixel in the plurality of frames, determining a brightness of the center pixel signal, determining the brightness of the plurality of neighboring pixel signals, determining if the brightness of the center pixel signal exceeds a wounded pixel threshold of the plurality of neighboring pixel signals, determining a location of the center pixel having the brightness greater than the wounded pixel threshold in at least one frame, determining a number of reoccurrences of the center pixel having the brightness greater than the wounded pixel threshold, determining if the number of reoccurrences exceeds a defect pixel threshold and updating the at least one center pixel signal to a mean of the plurality of neighboring pixel signals.

Abstract: A method of defect pixel correction, includes, receiving at least one center pixel signal in a plurality of frames, receiving a plurality of neighboring pixel signals adjacent the center pixel in the plurality of frames, determining a brightness of the center pixel signal, determining the brightness of the plurality of neighboring pixel signals, determining if the brightness of the center pixel signal exceeds a wounded pixel threshold of the plurality of neighboring pixel signals, determining a location of the center pixel having the brightness greater than the wounded pixel threshold in at least one frame, determining a number of reoccurrences of the center pixel having the brightness greater than the wounded pixel threshold, determining if the number of reoccurrences exceeds a defect pixel threshold and updating the at least one center pixel signal to a mean of the plurality of neighboring pixel signals.

Abstract: An example method of single-exposure multiple-frame image capture, making an HDR video, comprising, capturing a series of frames at an underexposed setting, selecting a reference image from the series of frames, aligning the series of frames to the reference image, merging the series of frames into one frame based on the reference image, indexing the reference image to a next frame in the series of frames, resulting in an indexed reference image, realigning the series of frames to the indexed reference image and remerging the series of frames into a single frame based on the indexed reference image.

Abstract: A method of multiple-exposure multiple-frame image capture, comprising, capturing a first group of frames at an underexposed setting, selecting a first key frame from the first group of frames, aligning the first group of frames to the first key frame, merging the first group of frames into one first frame, capturing a second group of frames at the underexposed setting, selecting a second key frame from the second group of frames, aligning the second group of frames to the second key frame, merging the second group of frames into one second frame, selecting a main group of frames based on the first key frame and the second key frame, aligning the first key frame and the second key frame based on the main group of frames and merging the first key frame and the second key frame based on the main group of frames.