Image Noise and Noise Images in Computer Vision, Photography and Embedded Vision
What is Image Noise?
Image Noise is the variation in signal from the ground trust, from pixel to pixel or frame to frame.
How Does A Noisy Image Impact Human Viewing and Computer Vision?
Noise is commonly thought of as Salt-and-Pepper noise. In a still image, one may say the image appears to be "Too Grainy"
In this sailboat example, researchers show that the confidence of class recognition drops rapidly multi-pixel / spatial noise is present in an image. Noise is closely tied to camera exposure settings; see our related guide, Camera Exposure and Computer Vision, for how exposure time and gain affect image quality.
Dodge et al. "Understanding How Image Quality Affects Deep Neural Networks"
What Types of Noise Exist in Cameras?
There are numerous types of noise in cameras. There are two main categories:
- Single pixel noise can be thought of as temporal or random. Single Pixel noise is a variation in signal (or pixel value) from frame to frame.
- Multi-pixel noise can be thought of as spatial or fixed pattern noise, with variation from pixel to pixel in a single frame.
The specific types of noise in a camera system are:
- Readout Noise
- Photon Shot Noise
- Fixed Pattern Noise
- Reset Noise
How much these noise sources matter depends on the sensor itself. See our image sensor selection guide for how resolution, shutter type, and pixel size factor into a sensor choice.
What About Denoising?
Denoising can be applied to mitigate noise. The approach is different for each of the two categories of noise.
Many multi-pixel (Spatial) denoising algorithms use a frequency filter to reduce the variation in pixel intensity. This filter also filters out information included in the image.
Hasinoff, et. al. "Burst Photography for high dynamic range and low light cameras"
Denoising (Spatial) Creates Texture Blur and Loss
Localized denoisingcreates texture loss, which is particularly visible with mobile phone cameras.
This texture loss is noticeable to the human visual system when zooming in on the image.
For computer vision, the impact can be even more noticeable depending on your filter size.
This example by Chen et al shows a brick wall building and the output from an edge detection network before and after filtering. The high frequency noise filter greatly reduces the texture in the image, particularly in regions where color is constant. This same tradeoff shows up when comparing sensor and lens choices; see our guide to spatial resolution in machine vision for how mm-per-pixel and the diffraction limit set the smallest detail a system can actually resolve.
References and Related Links
Dodge et al. "Understanding How Image Quality Affects Deep Neural Networks"
Stanford EE 392B lecture notes
Related Technical Guides
- Camera Exposure and Computer Vision, on how exposure time and gain interact with image noise.
- Motion Blur Photography and Shutter Speed, another temporal artifact that, like noise, degrades computer vision confidence.
- 12+ Image Quality Metrics that Impact Computer Vision, for how noise fits alongside sharpness, contrast, and distortion.
- Image Sensor Selection for Machine Vision, for resolution, shutter type, and pixel size tradeoffs that drive sensor noise performance.
Trying to Determine Your Camera Requirements?
Use our free web-based AoV Calculator to determine your system's Field of View Requirements. Then, use the M12 Lens calculator to match your requirements with the available lenses. Our Depth of Field Calculator also provides the hyperfocal distance and depth of field for every sensor and lens combination.
We also have a couple of other calculators that many engineers find interesting.
Angle of View calculator
Depth of Field calculator
Focal length (EFL) calculator
Field of View calculator
