These procedures are performed for all signals within the dynamic range of the sensor and can be calculated very fast (1–20s) using a standard computer. The temporal noise estimation for this signal value is the square root of the mean variance. From this array, pixels with equivalent signal value from the averaged image are chosen and their mean value is calculated. The array of squared standard deviations in the signals of each pixel is also calculated, giving the array of variances. We then register several shots of this scene and subsequently calculate the pointwise averaged image. These binary and grayscale transparencies form the test scene. An additional grayscale transparency located nearby allows the histogram of the registered image to be equalized. To accomplish this, we used a defocused image of a binary transparency with a contrast ratio exceeding the dynamic range of the camera. To achieve the best possible measurement accuracy, the intensity histogram of registered images should be close to uniform. This makes it possible to find not only full temporal noise, but also dark temporal noise and the dependency of light temporal noise on signal value. These constraints enable us to obtain the dependency of temporal noise on signal value for all possible values within the photosensor dynamic range. Using our procedure, we first create a scene (fixed target) with two simple constraints: the brightness dynamic range of a scene must exceed the dynamic range of a studied camera sensor and the non-uniformity of scene brightness must provide (in registered frames) the presence of all possible signal values within the dynamic range of the sensor. Our modification consists of two experimental measurement steps and three pure signal processing steps. Using our method, just two frames are required for noise measurement. We have proposed a modification of this method 4 that provides an unbiased estimation of the temporal noise, faster processing speeds, and separate measurements of dark and light temporal noise. Furthermore, researchers have failed to provide estimates of measurement errors, making its accuracy unknown. 3 This technique suffers from several significant drawbacks, however, such as an inability to measure noise over the full signal range and a failure to divide full temporal noise into light and dark components. Analysis of existing temporal noise measurement methods proves that noise measurement by automatic segmentation of non-uniform targets represents one of the fastest and simplest techniques. Additionally, if the dependency of the temporal noise on signal value is required in detail, thousands of shots are necessary, making such measurement extremely time-consuming. The most widely used among these is EMVA Standard 1288, 2 which allows accurate dark and light temporal noise measurement but can be technically difficult to implement. Knowledge of temporal noise is required in a number of optical digital systems, including wavefront coding for imaging (for aberration correction, extended depth of field, pattern recognition, and optical encryption), digital holography, image forensics, and increasing camera signal-to-noise ratio.Ĭamera sensor noise can be measured using a variety of techniques. Spatial noise is usually several magnitudes lower than temporal, to the extent that it can be neglected at first approximation. 1 Temporal noise varies randomly, whereas spatial noise has a pattern that arises due to sensor non-uniformities. Sensor noise, which can be divided into random and pattern components, represents one of the main factors limiting the amount of data that a camera can produce. Photo and video cameras are popular tools among both scientists and consumers.
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