Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Slide 23Slide 24Slide 25Slide 26Slide 27Slide 28Slide 29Slide 30Slide 31Slide 32Slide 33Slide 34Slide 35High Dynamic Range Imaging and Tone MappingPaul Debevec’s SIGGRAPH CourseThe Problem of Dynamic Range• Dynamic Range: Range of brightness values measurable with a camera(Hood 1986)High Exposure ImageLow Exposure Image• We need about 5-10 million values to store all brightnesses around us.• But, typical 8-bit cameras provide only 256 values!!• Today’s Cameras: Limited Dynamic RangeImages taken with a fish-eye lens of the sky show the wide range of brightnesses.High Dynamic Range Imaging• Capture a lot of images with different exposure settings.• Apply radiometric calibration to each camera.• Combine the calibrated images (for example, using averaging weighted by exposures).(Debevec)(Mitsunaga)SceneRadiance LLens ImageIrradiance E CameraElectronics Scene ImageIrradiance ERelationship between Scene and Image Brightness Measured Pixel Values, I Non-linear Mapping!Linear Mapping!• Before light hits the image plane:• After light hits the image plane: Can we go from measured pixel value, I, to scene radiance, L?Radiometric Calibration•Important preprocessing step for many vision and graphics algorithms such as photometric stereo, invariants, de-weathering, inverse rendering, image based rendering, etc.EIg :1•Use a color chart with precisely known reflectances.Irradiance = const * ReflectancePixel Values3.1%9.0%19.8%36.2%59.1%90%• Use more camera exposures to fill up the curve.• Method assumes constant lighting on all patches and works best when source is far away (example sunlight).• Unique inverse exists because g is monotonic and smooth for all cameras.02550 1g??1g[Greg Ward][Greg Ward][Greg
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