Multiple exposure high dynamic range image capture
View Patent ↗Techniques for creating a High Dynamic Range (HDR) image within a consumer grade digital camera from a series of images of a scene captured at different exposure levels, and displaying the HDR image on the camera's built-in display are provided. The approach employs mixing images of the series to incorporate both scene shadow and highlight details, and the removing of “ghost” image artifacts appearing in the mixed HDR image resulting from movement in the scene over the time the series images are captured. The low computational resource utilization of the image mixing and ghost removal processing operations, along with the ability to commence image mixing and ghost removal prior to the acquisition of all series images, can significantly reduce the time required to generate and display a tone mapped HDR image.
1. A method for detecting local motion between a reference image and a plurality of comparison images, comprising:
defining a number of luma thresholds that determine a group of reference image luma value ranges in which a particular comparison image is selected, the number corresponding to the number of comparison images;
defining a difference threshold function that sets threshold values according to luma values of the reference image;
for each particular comparison image, producing an intermediate detection map denoting local motion between the reference image and the particular comparison image by applying the threshold values on a difference image formed by comparing the reference image and the particular comparison image, wherein the intermediate detection map is based on pixels within a reference image luma value range that corresponds to the particular comparison image; and
outputting a final detection map that is a union of the intermediate detection maps.
2. The method of claim 1 wherein a relatively lower luma comparison image is selected in relatively higher reference image luma value ranges, and vice-versa.
3. The method of claim 1 wherein the luma threshold value set varies with difference image luma values.
4. The method of claim 1 wherein the intermediate detection map is produced by:
considering pixels with luma values in the range corresponding to the particular comparison image;
calculating a standard deviation and a mean of the considered pixels;
normalizing the considered pixels to have a same standard deviation and a same mean for the reference image and the particular comparison image;
creating an absolute difference map of the considered pixels; and
using the difference threshold function on the difference map to identify local motion detections.
5. A system for detecting local motion between a reference image and a plurality of comparison images, comprising:
a processor that:
defines a number of luma thresholds that determine a group of reference image luma value ranges in which a particular comparison image is selected, the number corresponding to the number of comparison images;
defines a difference threshold function that sets threshold values according to luma values of the reference image;
for each particular comparison image, produces an intermediate detection map denoting local motion between the reference image and the particular comparison image by applying the threshold values on a difference image formed by comparing the reference image and the particular comparison image, wherein the intermediate detection map is based on pixels within a reference image luma value range that corresponds to the particular comparison image; and
outputs a final detection map that is a union of the intermediate detection maps.
6. The system of claim 5 wherein a relatively lower luma comparison image is selected in relatively higher reference image luma value ranges, and vice-versa.
7. The system of claim 5 wherein the luma threshold value set varies with difference image luma values.
8. The system of claim 5 wherein the intermediate detection map is produced by:
considering pixels with luma values in the range corresponding to the particular comparison image;
calculating a standard deviation and a mean of the considered pixels;
normalizing the considered pixels to have a same standard deviation and a same mean for the reference image and the particular comparison image;
creating an absolute difference map of the considered pixels; and
using the difference threshold function on the difference map to identify local motion detections.