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Lets set up a scene which consists of a black trap, essentially reflecting very little light back to the camera, and a small shining metal ball, which will be a source of specular highlights, causing the small portion of the sensor to reach its saturation maximum.

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We will see that in fact signal-to-noise ratio for ISO 160 and ISO 200 are essentially the same especially if we are looking not just at the optically black area but at the image area as well, that is where some light is involved. For our case it is quite easy to demonstrate that not just noise at ISO 160 setting is lower, but the signal is lower too. The characteristic we are looking for is the signal-to-noise ratio. However it is not just the noise value that we are interested in. But are they of any use?įrom just looking at the noise values one can deduce that the lowest noise value is at ISO 160. It means that here we have some additional stage to form the data values for intermediate ISO settings they are not “native”. Meanwhile the noise values for ISO 100 and ISO 200 are very close. Dividing ISO 250 noise value by the noise value at ISO 200 we get the factor of 1.27. Dividing ISO 200 noise value by the noise value at ISO 160, the factor is 1.24. Lets look at the next slope formed by ISO 160 – ISO 200 – ISO 250. Incidentally 1/3 EV is equal to the cube root of 2 (21/3) = 1.26. Lets do some additional calculations.ĭividing Gdev at ISO 125 (6.8361) by Gdev at ISO 100 (5.4293) we can see that the noise is increased by the factor of 1.26 (6.8361/5.4293 = 1.26). This is enough to suspect that something out of ordinary is going on. Contrary to expectations the noise at ISO 160 is the lowest while noise at ISO 125 is higher than at ISO 400. The interesting part is the sawtooth between ISO 100 and ISO 1250. You can see that the plots for all four channels are extremely close. On this plot the X axis is ISO settings, the Y axis is noise on the logarithmic scale.

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Next, bring the first shot of the series into RawDigger and set RawDigger preferences to display the black frame (Display Options, Masked Pixels checkbox, checked) and not to subtract black Level (Data Processing, Subtract Black checkbox, unchecked). The subject of the shots can be anything – you can even shoot with a lens’ cap on. We are taking a series of shots at varying ISO settings, from the lowest to the highest and, of course, using all intermediate ISO settings available.

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It is covered from light, so it can be a good indicator of the lowest possible noise of the sensor, while noise is what we analyze to learn how to use a given sensor optimally. To demonstrate how this can be determined, we will first analyze the so-called Masked Pixels (often called optically black area, or simply OB), which is a portion of the sensor that we normally do not see in our images. Sometimes they are implemented the same way as the main ISO settings, but other times they are a result of certain manipulations, like digital multiplication. There is no single answer to this question, because it depends on implementation of these intermediate ISO settings in the particular camera. If one is shooting raw, they might be interested to know if there is any benefit in using intermediate ISO settings like ISO 125, 160, etc. I must warn our readers though – the below article is very technical and is not intended for beginners! Hope you enjoy it! Nasim. I had a chance to engage in a conversation with Iliah when discussing the noise performance of the Nikon Df, where he not only proved me wrong on my assumption that the Df had exactly the same sensor as the D4 (turns out that they are similar, but not exactly the same), but also shared some incredible information about testing procedures, data analysis and other crazy, mind-boggling stuff! The learning curve with photography never ends, especially when you get into the whole sensor and image processing pipeline side of it.

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I guess today is a “blow your mind” Friday, because we have a guest post here by Iliah Borg, the person behind the RawDigger software that is used to analyze RAW images.