Ghost-Free High Dynamic Range Imaging

Abstract

Abstract. Most high dynamic range image (HDRI) algorithms assume stationary scene for registering multiple images which are taken under different exposure settings. In practice, however, there can be some global or local movements between images caused by either camera or object motions. This situation usually causes ghost artifacts which make the same object appear multiple times in the resultant HDRI. To solve this problem, most conventional algorithms conduct ghost detection procedures followed by ghost region filling with the estimated radiance values. However, usually these methods largely depend on the accuracy of the ghost detection results, and thus often suffer from color artifacts around the ghost regions. In this paper, we propose a new robust ghost-free HDRI generation algorithm that does not require accurate ghost detection and not suffer from the color artifact problem. To deal with the ghost problem, our algorithm utilizes the global intensity transfer functions obtained from joint probability density functions (pdfs) between different exposure images. Then, to estimate reliable radiance values, we employ a generalized weighted filtering technique using the global intensity transfer functions. Experimental results show that our method produces the state-of-the-art performance in generating ghost-free HDR images.

Robust patch-based hdr reconstruction of dynamic scenes

“…Eden et al [2006] used the distance of an exposure's radiance to that of a reference to select a single exposure for each pixel. Heo et al [2010] computed the joint probability density function between exposures to map values from one exposure to another, and then used the Gaussian-weighted distance to a reference value to weight each exposure during merging.…”

HDR Deghosting: How to Deal with Saturation?

“…Khan et al [14] use kernel density estimators to compute the probability that a pixel belongs to the background and weight the pixel based on the computed probability. Heo et al [10] use a weight that emphasizes well-exposed pixels and a second weight that enforces consistency across spatial and exposure domains. Zhang and Cham [29] propose to weight the pixel using local gradients across the exposure stack as a measure of consistency.…”

Full high-dynamic range images for dynamic scenes

“…Another approach is based on scale invariant feature transform (SIFT). 18,25 A modified SIFT algorithm is used to extract key point descriptors that represent correspondences between key points in the reference image and the remaining images. After finding SIFT features, homographies are calculated using RANSAC algorithm to match all images and a reference image previously selected.…”

“…[13][14][15][16][17][18] In the first case, the result is not coherent with the scene because the dynamic objects are missing in the final image. In the second case there are over and under exposed areas in the HDR image because areas affected by movement are replaced with LDR content from the best exposed image only.…”