When it comes to post-processing, there are options to remove noise as well as to add noise. This is yet another contradiction that every photographer faces once in a while.

Noise in photography refers to the small specks which become visible at high ISOs. Sometimes noise can also refer to areas of discoloration. In the film days, the higher ISO films had ‘grain’. Noise is unwanted but grains can add character to the photograph. This statement should clear all the doubts. Read it again if you want to. Unfortunately a lot of image editing programs call this ‘grain’ also noise and thus the confusion.

What is Noise?

The sensor is an electronic device which converts light into electrical signals. This is recorded as a photograph. When the light is low, the sensor along with the supporting electronics has to work hard to get the light converted to electrical signal. This causes artifacts too. This is noise. It can be due to various factors at play but at the end it robs the image of the clarity and even causes change in saturation or color balance. With advancement of technology, cameras are now capable of capturing reasonably fine images at insanely high ISOs. Such ISOs were unthinkable with the film.

(A noisy photograph! Noise is the biggest foe for bird photographers, since they are forced to use high ISOs in order to get proper exposures. Yellow Vented Himalayan Bulbul photographed at Natadol. ISO 8000)

Noise which mostly affects the image, which we want to control, is due to the following main reasons –

1. Noise due to sensor heat – This is self explanatory. The sensor works hard to convert the light falling on to it into electrical signals. Even in the absence of light, if the sensor is active, it gives out heat and this thermal energy or heat is captured as noise.
2. Sometimes there are specific sensor cells or pixels which heat up quickly and stand out as bright colored dots. These are called hot pixels and can be quite commonly seen in long exposure times.
3. Noise due to irregular amount of light hitting the sensor – By far, this is the commonest contributor of noise that I know of. When the exposures are short during low light conditions (like night shots), the total amount of light that forms an impression on the sensor is small and irregular. (An analogy that I read somewhere – it is like spraying a plant with water. When you spray for a short duration, the water droplets don’t cover it equally and there are areas left which are dry and some areas with more than one water droplet.) This leads to irregularities in the amount of light per pixel and causes noise. With improvement in electronics, this is being addressed and now the new cameras have reduced this type of noise. ETTR or Expose to the right is a work around to this noise. The amount of useful light increases when the image is overexposed but the noise does not increase proportionately. Astro-photographers call this shot-noise and this happens to be their biggest enemy in terms of noise in their field of photography.
4. Noise due to camera electronics – Apart from the sensor, the various activities by camera electronics also generate noise. The most notorious of them is when the data is being read from the sensor. This becomes pretty problematic in short exposures.

Apart from these four reasons, there are many others too which cause noise in the images.

(Example of noise seen at high ISOs. Compare it with the photograph below which show grain that was added after reducing the noise during post-processing)

(Grain added after cleaning up the noise. Notice the fine texture and equal spread all over the image)

(For those of you wondering from the earlier two examples, this image was created after removing the noise but before adding grain, an intermediate step)

Clicking Photographs with Low Noise

When you press the shutter release button, the decision is taken about the noise that is going to be captured with the photograph. So, to keep the noise at bay, use these pointers-

• When you capture an image, use a low ISO whenever possible… this is easy and yet people forget or ignore this. For most photographs this works well. Why most and not for all? I’ll be writing about this sometime in future.
• Now something quite different from the above point – in new generation cameras, the lowest ISO is not the one with lowest noise. Here the aim should be to use native ISO or the one which gives the least noise, especially when capturing night shots or when the image is intended to be processed as a monochrome. Going above lowest ISO is not ideal apart from low noise thingy. High ISO can compromise colors.
• Expose the image to the right side of the histogram (slight overexposure but without loosing the details) and correct the exposure in post-processing. (Understanding Histograms). In photography world, this is also called ETTR or Expose To The Right.
• Switch off the camera in between shots and keep it off until the next planned shot. This gives time for the electronics to cool down. Did you know that the camera is less prone for noise in winters than summers? Switch off the long exposure noise reduction for regular photography. I have seen this feature work only in exposures that extend for more than a minute.

Post-processing to reduce noise

The specks are not equally distributed in the three colors. In most sensors, the blue and green specks are more than the red ones. So instead of reducing all color specks, try to reduce noise for only these colors if your post-processing software has this option. The size for noise reduction should be kept small enough just to cover the specks. Remember reduction in noise also means loss of some details. The aim therefore is to reduce the noise while keeping the details intact. The aim is also to get an acceptable image at the end. Do not loose sight of the forest while looking for the trees. When you are removing noise at 100% or higher magnification, keep coming back to view the whole image for a better understanding of the overall photograph.

Next comes the noise that is in the same color as the subject but it varies in intensity. This is called luminance noise and some programs have dedicated option to reduce this kind of noise. This should be done after removing the colored noise specks.

Another good option to use is Nik Collection (which was once owned by Google too). This set of plugins has a ‘Dfine’ tool, which has a very good algorithm for noise reduction and makes the process very easy to use. Do give it a try if the Nik Collection is available for your favorite image editing program.

As I had said before, removal of noise causes some loss of details too. Sharpening can be applied to cover some of the loss. Make sure not to overdo the sharpening. Sometimes when the sharpening is not applied properly, it will end up increasing the noise which you had initially reduced. Do check this out for proper way of sharpening the image – Sharpening

Adding Grains (Many consider this opposite of noise reduction or simply ‘nostalgic madness’)

Addition of artifacts to replicate film grain is called ‘addition of noise’. This is what makes the whole thing confusing. For clarity sake, this should have been called ‘grain’ everywhere but then not all programmers are photographers.

Addition of grain makes the image look sharper. Grains also add a character to the photograph. This may have been a left-over in our subconscious since the film days but it does make the photographs look more pleasing. Another word of cautions – if you are planning to submit your pictures to stock-image sites, keep the addition of grains low. Their algorithms can sometimes reject a photograph on technical grounds disregarding the overall artistic impact the grain would have had.

For some more pointers on post-processing – Post Processing RAW. This link also contains a photograph that I had shot at 4000 ISO after noise reduction.

(A confession – I rarely click photographs at very high ISOs. Most of my images are clicked at around the native ISO of my camera with mild ETTR. I do not enjoy sitting long hours, doing extensive post-processing. Inserted the photograph here to add color to the article.)