TienDaiIFHDR: Difference between revisions
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Dynamic range of a scene is defined as the ratio of the highest to the lowest luminance. The real world scenes often have a very wide range of luminance, sometimes exceeding 10 orders of magnitude. Fig. 1 shows an high dynamic range ( HDR ) scene with a dynamic range of about 167, 470:1. To reproduce these scenes presents a challenge for conventional digital capture and display devices, which suffer a limited dynamic range of only 2 orders of magnitude. The most common solution is to take a sequence of low dynamic range ( LDR ) images of the same scene under different exposure intervals to capture all the radiance information. There are two pipelines to prepare the image stack for display. One way is to utilize them to firstly estimate the camera response function and recover the true radiance of the original scene ( recorded as a 32 bit float radiance map) [ 1, 2 ], and then tone map the created radiance map for display on LDR reproduction media ( usually 8 bit per channel ) [ 3, 4, 5 ]. Although this was gives very satisfying result, it's computation expensive and time consuming. The other way is to fuse these images directly without the intermediate step of creating radiance map [ 6, 7 ]. This method is usually referred as "Exposure Fusion" [7]. Exposure fusion produces HDR-like images, which are comparable to those tone-mapped results, at a much lower computational cost. Due to its effectiveness and computational efficiency, exposure fusion is adopted by most of applications to deal with HDR on mobile platform, which has limited computational power [8]. | Dynamic range of a scene is defined as the ratio of the highest to the lowest luminance. The real world scenes often have a very wide range of luminance, sometimes exceeding 10 orders of magnitude. Fig. 1 shows an high dynamic range ( HDR ) scene with a dynamic range of about 167, 470:1. To reproduce these scenes presents a challenge for conventional digital capture and display devices, which suffer a limited dynamic range of only 2 orders of magnitude. The most common solution is to take a sequence of low dynamic range ( LDR ) images of the same scene under different exposure intervals to capture all the radiance information. There are two pipelines to prepare the image stack for display. One way is to utilize them to firstly estimate the camera response function and recover the true radiance of the original scene ( recorded as a 32 bit float radiance map) [ 1, 2 ], and then tone map the created radiance map for display on LDR reproduction media ( usually 8 bit per channel ) [ 3, 4, 5 ]. Although this was gives very satisfying result, it's computation expensive and time consuming. The other way is to fuse these images directly without the intermediate step of creating radiance map [ 6, 7 ]. This method is usually referred as "Exposure Fusion" [7]. Exposure fusion produces HDR-like images, which are comparable to those tone-mapped results, at a much lower computational cost. Due to its effectiveness and computational efficiency, exposure fusion is adopted by most of applications to deal with HDR on mobile platform, which has limited computational power [8]. | ||
== Methods == | == Methods == | ||
Revision as of 23:35, 18 March 2012
Project Title
Image Fusion for High Dynamic Range/ All-in-focus Applications
Introduction
Dynamic range of a scene is defined as the ratio of the highest to the lowest luminance. The real world scenes often have a very wide range of luminance, sometimes exceeding 10 orders of magnitude. Fig. 1 shows an high dynamic range ( HDR ) scene with a dynamic range of about 167, 470:1. To reproduce these scenes presents a challenge for conventional digital capture and display devices, which suffer a limited dynamic range of only 2 orders of magnitude. The most common solution is to take a sequence of low dynamic range ( LDR ) images of the same scene under different exposure intervals to capture all the radiance information. There are two pipelines to prepare the image stack for display. One way is to utilize them to firstly estimate the camera response function and recover the true radiance of the original scene ( recorded as a 32 bit float radiance map) [ 1, 2 ], and then tone map the created radiance map for display on LDR reproduction media ( usually 8 bit per channel ) [ 3, 4, 5 ]. Although this was gives very satisfying result, it's computation expensive and time consuming. The other way is to fuse these images directly without the intermediate step of creating radiance map [ 6, 7 ]. This method is usually referred as "Exposure Fusion" [7]. Exposure fusion produces HDR-like images, which are comparable to those tone-mapped results, at a much lower computational cost. Due to its effectiveness and computational efficiency, exposure fusion is adopted by most of applications to deal with HDR on mobile platform, which has limited computational power [8].
Methods
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Results
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Conclusions
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References
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Appendix I - Code and Data
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Appendix II - Work Partition
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