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Computational photography combines plentiful computing, digital sensors, modern optics, actuators, probes and smart lights to escape the limitations of traditional film cameras and enables novel imaging applications. Unbounded dynamic range, variable focus, resolution, and depth of field, hints about shape, reflectance, and lighting, and new interactive forms of photos that are partly snapshots and partly videos are just some of the new applications found in Computational Photography.
Computational photography extends digital photography by providing the capability to record much more information and by offering the possibility of processing this information afterward.
The computational techniques encompass methods from modification of imaging parameters during capture to sophisticated reconstructions from indirect measurements. Many ideas in computational photography are still relatively new to digital artists and programmers. A larger problem is that a multi-disciplinary field that combines ideas from computational methods and modern digital photography involves a steep learning curve.
For example, photographers are not always familiar with advanced algorithms now emerging to capture high dynamic range images, but image processing researchers face difficulty in understanding the capture and noise issues in digital cameras. The new capture methods include sophisticated sensors, electromechanical actuators and on-board processing. The methods can achieve a 'photomontage' by optimally fusing information from multiple images, improve signal to noise ratio and extract scene features such as depth edges.
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i want a ppt for computational photography