There are a few other sources of noise that were not described in the previous section, for example, Fixed Pattern Noise – which is an estimate of the spatial non-uniformities across the imaging area. QE = the Quantum Efficiency (QE, the probability of photons being converted into electrons, expressed as a % and having a significant dependence on the wavelength of incident light)Įstimating the Total Noise in an imaging system is somewhat more complex, since we have to account for all the sources of noise: Photon Shot Noise, Read Noise & Dark Shot Noise. In this case, the estimated signal (in electrons) would be S = P * a * QE * t, where “a” is the area of a pixel, easily estimated from the pixel dimensions that can be obtained from the datasheet of a camera. Note that we could model this type of experiment by simply choosing different unit for “P”: if we wanted to take the pixel dimensions of different cameras into consideration, we could use P to represent photons/ sq-um/sec instead of photons/ pixel/sec. This doesn’t represent a typical experiment, in which the optics remain the same, while cameras with different pixel sizes are swapped in and out for evaluation. Defining “P” in these units carries with it an implicit assumption that the same number of Photons fall on each pixel of the different cameras being compared, even if the pixels of different cameras happened to be of different sizes. P = the light level, represented by the Photon Flux in photons/pixel/sec. We can estimate the value of the Signal (in electrons) in terms of camera parameters such as its Quantum Efficiency and camera settings such as the Duration of Exposure. SNR is simply the Signal, divided by the Total Noise. In fact, one can use the estimated SNR under typical conditions of an experiment to identify the camera technology that is most suitable for acquiring quantitatively useful images. This method is applicable to the selection of components. In a similar manner, SNR can be useful as a figure-of-merit for image quality in comparing two or more different imaging systems under similar conditions. While doing so, we may also be required to consider the effect of bright light on the longevity of fragile samples and/or the effects of photobleaching in applications such as fluorescence microscopy. This allows us to make informed decisions in our Design of Experiments – by examining the tradeoffs, we may arrive at a combination of parameters, such as the brightness of a light source, the exposure setting of a camera that optimizes SNR. Or, having estimated the different SNRs under different illumination conditions, we can observe the tradeoffs with respect to the effect of increasing the brightness of the light source on our samples, or of the cost of the system. We can estimate the SNR in each case and then evaluate other tradeoffs, such as the time taken for each exposure – which affects the overall throughput of our workflow. under different exposure settings, or perhaps under different illumination conditions. This points to its use as a method of quantitatively evaluating an imaging system under different conditions, e.g. The LOWER SNR image was obtained with a short=2.5ms exposure and the HIGHER SNR image was obtained with a long=1sec exposure.įrom the above images, we can see how SNR is useful as a figure of merit when comparing the image quality of two images. For inspection and detection applications in which an image is used to discern or quantify features that are difficult to see, a higher SNR can improve the detection threshold.Ī fluorescence slide was imaged with the same excitation & filters, but with different camera exposures. An image with a higher SNR permits a finer quantization of data which can be important in detecting small differences in the grayscale values within an image, or between multiple images. For applications in which an image is a source of data that is analyzed by image processing algorithms, a higher SNR can be a critical requirement because it improves the accuracy and repeatability of the process. It provides a way to quantitatively compare images, because a higher SNR usually correlates with an observable improvement in image quality. The Signal-to-Noise Ratio (SNR) is simply the Signal divided by the Total Noise in a system: it is a convenient figure-of-merit to evaluate how well the signal overcomes the noise in the system under a particular set of conditions. Previous Article Back to Topics Next Article
0 Comments
Leave a Reply. |