{"id":108912,"date":"2019-01-28T07:00:57","date_gmt":"2019-01-28T13:00:57","guid":{"rendered":"https:\/\/www.premiumbeat.com\/blog\/?p=108912"},"modified":"2019-01-27T11:57:21","modified_gmt":"2019-01-27T17:57:21","slug":"controlling-high-medium-low-noise-frequencies","status":"publish","type":"post","link":"https:\/\/www.premiumbeat.com\/blog\/controlling-high-medium-low-noise-frequencies\/","title":{"rendered":"Controlling High, Medium, and Low Image Noise Frequencies"},"content":{"rendered":"

Every filmmaker is familiar with image noise, but what are its frequencies, and what’s really going on when we clean it up?<\/h2>\n

Video footage at its most fundamental is the recording, storing, and playback of light waves reflecting off of objects in the frame. The light waves then strike one of millions of photo-sensitive pixels that stores the charge of the wave before transferring that data to specialized hardware for interpretation and assembly of the final image.<\/p>\n

As a result of this imaging process, each pixel is subject to faulty inputs caused by any number of sources of image noise<\/a>. Ranging from cosmic radiation to over-volting the sensor, all noise essentially overwrites the information you were trying to capture at each affected pixel.<\/p>\n

There are plenty of methods for handling this noise, both at the hardware and software<\/a> levels, but the technology behind these tools goes largely unexplored by the average filmmaker.<\/p>\n

Today, we’re taking the scenic route. Let’s take an in-depth look at one of the most prevalent noise reduction techniques and see if there is a way to mimic its results manually. At the very least, we should get a better idea for what our tools are doing under the hood.<\/p>\n

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Spatial Noise Reduction<\/h3>\n

Most video noise reduction techniques fall into one of two categories: spatial<\/strong> and temporal<\/strong>. As the names suggest, spatial noise reduction<\/em> deals with noise across each pixel of each frame, whereas temporal noise reduction<\/em> examines multiple frames to determine whether pixel readout is intended or a result of image noise.<\/p>\n

Because spatial noise reduction addresses each frame individually, its results should be the easiest to replicate manually.<\/p>\n

The primary way most programs implement spatial noise reduction is by analyzing the frequencies of the recorded light from each pixel block. This is because noise has different characteristics depending on the frequency of the affected pixel.<\/p>\n

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High, Medium, and Low Noise Frequencies<\/h3>\n

Regardless of type, all video noise breaks down into one of three categories based on how the noise manifests in the footage: high-<\/strong>, medium-<\/strong>, or low-frequency<\/strong>.<\/p>\n

\"Controlling<\/a><\/p>\n

The sharp, staticy nature of high-frequency noise is usually the most noticeable, but patchy medium- and splotchy low-frequency noise will affect your footage just as much.<\/p>\n

Let’s look at two clips with noise problems and manually select and bring down the visibility of the noise.<\/p>\n

\"Controlling<\/a><\/p>\n

We’re starting out with an example from a short I shot on super 16 several years back. I underexposed it a fair bit for effect, but during the film transfer, the film grain turned into irritating digital noise across most of the frame.<\/p>\n

I’ve brought the exposure up a bit for the actress \u2014 and also to make spotting the noise frequencies a bit easier. For this shot, we’ll bring up the levels on our actress and then do our best to turn all of the noisy pixels to black.<\/p>\n

Selecting<\/h4>\n

The tool we’re using to select specific regions of noise today is the HSL\/Secondary<\/a><\/strong> tab in the Lumetri Color Panel.<\/p>\n

When selecting your footage, make sure to ask yourself a few questions:<\/p>\n