1. Fast Bilateral Filtering for the Display of High-Dynamic-Range Images
Frédo Durand & Julie Dorsey
Laboratory for Computer Science
Massachusetts Institute of Technology

2. Tone mapping 10-6 106 10-6 106 Pixel value 0 to 255
How can we display it?
Linear scaling?, thresholding?
10-6
106
dynamic range
Real world
radiance
10-6
106
Display
intensity
Pixel value 0 to 255
CRT has 300:1 dynamic range

3. High Dynamic Range Images
Here’s an HDR image that we constructed from the photographs you saw on the first slide. We’ll use this example throughout our talk. The dynamic range here is 25,000:1, which is much higher than the dynamic range of this video projector, so to visualize this image we show the logarithm of the luminance in pseudo-color.
This image is very rich in detail, which is spread across the entire dynamic range. (click)
For example, this slice of the dynamic range contains much detail in the darker regions of the scene, such as the leaves of the plant and the bulletin board. (click)
This slice contains the details on the shadowed stone tiles outside (click)
And this contains the luminances in some of the sunlit regions.
Summary sentence: it can be a challenge to try to display all this wealth of visual information in a single image.
low

4. Eye is not a photometer!
"Every light is a shade, compared to the higher lights, till you come to the sun; and every shade is a light, compared to the deeper shades, till you come to the night."
— John Ruskin, 1879

5. A typical photo
Sun is overexposed
Foreground is underexposed

6. Edge-preserving filtering
Blur, but not across edges
Anisotropic diffusion [Perona & Malik 90]
Blurring as heat flow
LCIS [Tumblin & Turk]
Bilateral filtering [Tomasi & Manduci, 98]
Input
Gaussian blur
Edge-preserving

7. Comparison with our approach
We use only 2 scales
Can be seen as illumination and reflectance
Different edge-preserving filter from LCIS
Large-scale
Detail
Output
Compressed

8. Our approach Do not blur across edges Non-linear filtering Large-scale
Output
Detail
Color