Extending dynamic range of monochrome and color images through fusion

Bogoni, Luca

doi:10.1109/icpr.2000.903475

Cited by 96 publications

(49 citation statements)

References 7 publications

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“…Bogoni, after global registration, uses optical flow estimation as a means of per-pixel registration so that the radiance values from the images in the stack can be correctly combined [2]. Kang et al take a similar approach and extend it to produce HDR video sequences [9].…”

Section: Artifact Removalmentioning

confidence: 99%

Artifact-free High Dynamic Range imaging

Gallo

Gelfandz

Chen

et al. 2009

2009 IEEE International Conference on Computational Photography (ICCP)

205

135

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Single best exposure tone-mapped Standard HDR Our result Figure 1. An exposure stack taken at a sculpture garden. In the first row from left to right: single best exposure tone-mapped, standard HDR, and our result. Our result captures all the range of the scene while being free of ghosting. The second row shows the original exposures in the stack. AbstractThe contrast in real world scenes is often beyond what consumer cameras can capture. For these situations, High Dynamic Range (HDR) images can be generated by taking multiple exposures of the same scene. When fusing information from different images, however, the slightest change in the scene can generate artifacts which dramatically limit the potential of this solution. We present a technique capable of dealing with a large amount of movement in the scene: we find, in all the available exposures, patches consistent with a reference image previously selected from the stack. We generate the HDR image by averaging the radiance estimates of all such regions and we compensate for camera calibration errors by removing potential seams. We show that our method works even in cases when many moving objects cover large regions of the scene.

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Section: Artifact Removalmentioning

confidence: 99%

Artifact-free High Dynamic Range imaging

Gallo

Gelfandz

Chen

et al. 2009

2009 IEEE International Conference on Computational Photography (ICCP)

205

135

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“…On the other hand, Bogoni [12] estimated motion vectors using optical flow for different exposure images, and then used those information to warp other exposure images. Kang et.…”

Section: Previous Workmentioning

confidence: 99%

Ghost-Free High Dynamic Range Imaging

Heo

Lee

et al. 2011

Lecture Notes in Computer Science

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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.

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“…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. There are also approaches for tracking dynamic objects through the sequence [19][20][21] but their results are still limited.…”

Section: Introductionmentioning

confidence: 99%

Full high-dynamic range images for dynamic scenes

et al. 2012

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The limited dynamic range of digital images can be extended by composing photographs of the same scene taken with the same camera at the same view point at different exposure times. This is a standard procedure for static scenes but a challenging task for dynamic ones. Several methods have been presented but few recover high dynamic range within moving areas. We present a method to recover full high dynamic range (HDR) images from dynamic scenes, even in moving regions. Our method has 3 steps. Firstly, areas affected by motion are detected to generate a ghost mask. Secondly, we register dynamic objects over a reference image (the best exposed image in the input sequence). Thirdly, we combine the registered input photographs to recover HDR values in a whole image using a weighted average function. Once matching is found, the assembling step guarantees that all aligned pixels will contribute to the final result, including dynamic content. Tests were made on more than 20 sets of sequences, with moving cars or pedestrians and different background. Our results show that Image Mapping Function approach detects best motion regions while Normalized Cross Correlation offers the best deal speed-accuracy for image registration. Results from our method offers better result when moving object are roughly rigid and their movement is mostly rigid. The final composition is an HDR image with no ghosting and all dynamic content present in HDR values.

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Extending dynamic range of monochrome and color images through fusion

Cited by 96 publications

References 7 publications

Artifact-free High Dynamic Range imaging

Artifact-free High Dynamic Range imaging

Ghost-Free High Dynamic Range Imaging

Full high-dynamic range images for dynamic scenes

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