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If you’ve ever seen a picture where you notice dust particles that are not part of the actual image, you’re probably seeing ‘noise’ in the image. There are many technical reasons for why this happens. It often obscures the actual image and is the leading cause of image quality degradation in digital image transmission.
This is where image processing offers a robust solution. It provides a wide range of noise reduction techniques, such as spatial filtering, frequency filtering, transformation-based filtering, deep learning-based filtering, etc.
In this article, we’ll explore some key techniques that can be used to reduce noise in images, along with investigating the leading types and causes of image noise. Let’s dive in!
A simulation of noise variations – Mdf, CC BY-SA 3.0, via Wikimedia Commons
Factors ranging from environmental conditions to the camera’s sensor can introduce noise into the image. The four main types of noise that you usually see in images include:
Image noise can result from various sources, including:
Noise models in image processing serve as mathematical representations of the different kinds of noise that can affect images. These models help understand the occurrence of different kinds of noise through simulations, which in turn helps develop strategies to reduce it.
Some common noise models include:
In image analysis, noise assessment and evaluation is a fundamental task. It involves quantifying the level of noise in an image. This process relies on two primary noise measurement techniques:
Noise makes images grainy and discolored, obscuring fine details. To neutralize this effect, noise reduction techniques help improve image quality for better outcomes in many domains like photography, security, video conferencing, surveillance, etc. For example, noise reduction is critical for accurate diagnosis and treatment planning in medical imagery.
The noise reduction techniques work best under conditions like low light, high ISO settings, rapid shutter speeds, or when dealing with inherently noisy cameras.
Some common noise reduction techniques include:
Noise reduction has a variety of applications across industries, such as image restoration and image upscaling, but the most important ones are:
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Haziqa is a Data Scientist with extensive experience in writing technical content for AI and SaaS companies.
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