Wide dynamic range imaging
نویسندگان
چکیده
Interest in technologies that can help improve image sensors—in terms of cost, noise and dynamic range—is growing as quickly as the market for them. Dynamic range (DR) quantifies the ability of a sensor to image highlights and shadows. If we define the dynamic range of the sensor as 20 log(S/N), where S is the maximal signal value and N is the sensor noise, the typical image sensors will have a very limited dynamic range, about 65–75dB. Thus, dynamic range can be increased in two ways: noise reduction can expand dynamic range for darker regions, and raising the incident-light saturation level can improve it for brighter regions. The human visual system (HVS) has overall dynamic range of about 200dB, but it cannot operate over the entire (enormous) range at the same time. Using the phenomenon called brightness adaptation, it adapts for various conditions by changing its overall sensitivity. Thus, it still has a high dynamic range even at a single adaptation level. This is achieved with the assistance of three major mechanisms:1 change in pupil diameter, neural adaptation, and photochemical adaptation. It is obvious that these HVSmechanisms are very complicated and cannot be exactly emulated. Nevertheless, we can try to approach some of them by designing smart image sensors with algorithms that allow an adaptive widening of the dynamic range. The overall task for WDR imaging can be divided into two distinguished stages: image capture, preventing the loss of scene details; and image compression, allowing image representation on conventional computer screens. The first stage is a challenge mainly for image-sensor designers, while the second stage can be performed also in software.
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