History
HDRI was originally developed for use with purely computer-generated images. Later, methods were developed to produce a high dynamic range image from a set of photographs taken with a range of exposures. With the rising popularity of digital cameras and easy-to-use desktop software, the term “HDR” is now popularly used to refer to the process of tone mapping together with bracketed exposures of normal digital images, giving the end result a high, often exaggerated dynamic range. This composite technique is different from, and generally of lower quality than, the production of an image from a single exposure of a sensor that has a native high dynamic range. Tone mapping is also used to display HDR images on devices with a low native dynamic range, such as a computer screen.
One problem with HDR has always been in viewing the images. Mundane CRTs, LCDs, prints, and other methods of displaying images only have a limited dynamic range. Thus various methods of converting HDR images into a viewable format have been developed, generally called “tone mapping”.
Early methods of tone mapping were simple. They simply showed a “window” of the entire dynamic range, clipping to set minimum and maximum values. However, more recent methods have attempted to show more of the dynamic range. The more complex methods tap into research on how the human eye and visual cortex perceive a scene, trying to show the whole dynamic range while retaining realistic colour and contrast.
Dynamic Range
Dynamic range in photography describes the ratio between the maximum and minimum measurable light intensities (white and black, respectively). In the real world, one never encounters true white or black only varying degrees of light source intensity and subject reflectivity. Therefore the concept of dynamic range becomes more complicated, and depends on whether you are describing a capture device (such as a camera or scanner), a display device (such as a print or computer display), or the subject itself. High dynamic range (HDR) images enable photographers to record a greater range of tonal detail than a given camera could capture in a single photo. This opens up a whole new set of lighting possibilities which one might have previously avoided for purely technical reasons.
Conclusion :
The dynamic range is the ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to.
- For a scene: ratio between the brightest and darkest parts of the scene.
- For a camera: ratio of saturation to noise. More specifically, ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise.
- For a display: ratio between the maximum and minimum intensities emitted from the screen.
HDR Images
The Dynamic Range of real-world scenes can be quite high ratios of 100,000:1 are common in the natural world. An HDR (High Dynamic Range) image stores pixel values that span the whole tonal range of real-world scenes. Therefore, an HDR image is encoded in a format that allows the largest range of values, e.g. floating-point values stored with 32 bits per color channel.
Another characteristics of an HDR image is that it stores linear values. This means that the value of a pixel from an HDR image is proportional to the amount of light measured by the camera. In this sense, HDR images are scene-referred, representing the original light values captured for the scene. Whether an image may be considered High or Low Dynamic Range depends on several factors. Most often, the distinction is made depending on the number of bits per color channel that the digitized image can hold. However, the number of bits itself may be a misleading indication of the real dynamic range that the image reproduces converting a Low Dynamic Range image to a higher bit depth does not change its dynamic range, of course.
· 8-bit images (i.e. 24 bits per pixel for a color image) are considered Low Dynamic Range.
· 16-bit images (i.e. 48 bits per pixel for a color image) resulting from RAW conversion are still considered Low Dynamic Range, even though the range of values they can encode is much higher than for 8-bit images (65536 versus 256). Converting a RAW file involves applying a tonal curve that compresses the dynamic range of the RAW data so that the converted image shows correctly on low dynamic range monitors. The need to adapt the output image file to the dynamic range of the display is the factor that dictates how much the dynamic range is compressed, not the output bit-depth. By using 16 instead of 8 bits, you will gain precision but you will not gain dynamic range.
· 32-bit images (i.e. 96 bits per pixel for a color image) are considered High Dynamic Range. Unlike 8- and 16-bit images which can take a finite number of values, 32-bit images are coded using floating point numbers, which means the values they can take is unlimited. It is important to note, though, that storing an image in a 32-bit HDR format is a necessary condition for an HDR image but not a sufficient one. When an image comes from a single capture with a standard camera, it will remain a Low Dynamic Range image, regardless of the format used to store it.
There are various formats available to store HDR images, such as Radiance RGBE (.hdr) and OpenEXR (.exr) among the most commonly used.
The new “merge to HDR” feature of Adobe Photoshop CS2 or latest allows the photographer to combine a series of bracketed exposures into a single image which encompasses the tonal detail of the entire series. There is no free lunch however; trying to broaden the tonal range will inevitably come at the expense of decreased contrast in some tones. Learning to use the merge to HDR feature in Photoshop CS2 can help you make the most of your dynamic range under tricky lighting while still balancing this trade off with contrast.
The other HDR softwares are Photomatix or FDR Tools
How do I shoot an HDR image
Most digital cameras are only able to capture a limited dynamic range (the exposure setting determines which part of the total dynamic range will be captured). This is why HDR images are commonly created from photos of the same scene taken under different exposure levels.
Here are some recommendations for taking different exposures for the HDR image:
1. Mount your camera on a tripod, this is recommended to reduce camera shaking
2. Set your camera to manual exposure mode. Select an appropriate aperture for your scene (e.g. f/8 or less if you need more depth of field) and the lowest ISO setting.
3. Measure the light in the brightest part of your scene (spot metering or in Av mode to point only the highlights) and note the exposure time. Do the same for the darkest shadows of your scene.
4. Determine the number and value of exposures necessary. For this, take as a basis the exposure time measured for the highlights. Multiply this number by 4 to find the next exposure with a stop spacing of 2 EV. Multiply by 4 successively for the next exposures till you pass the exposure measured for the shadows. (Note: For most daylight outdoor scenes excluding the sun, 3 exposures spaced by two EVs are often sufficient to properly cover the dynamic range).
5. You can make use of Auto-Exposure Bracketing if your camera supports it and if it allows a sufficient exposure increment and number of auto-bracketed frames to cover the dynamic range determined in step 4. Otherwise, you will have to vary the exposure times manually.
I would suggest only using HDR images when the scene’s brightness distribution can no longer be easily blended using a graduated neutral density (GND) filter. This is because GND filters extend dynamic range while still maintaining local contrast. Scenes which are ideally suited for GND filters are those with simple lighting geometries, such as the linear blend from dark to light encountered commonly in landscape photography (corresponding to the relatively dark land transitioning into bright sky).
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