Noise in digital images is primarily caused by high ISO settings. When the ISO is increased, the camera's sensor becomes more sensitive to light, which amplifies both the signal and the noise.

Gaussian noise is characterized by random, pixel-level variations in brightness. It is often referred to as "white noise" due to its uniform distribution across the image.

The primary purpose of noise reduction techniques is to remove unwanted noise from an image, resulting in a cleaner and more visually appealing result.

Median filter is a noise reduction technique that involves averaging the values of neighboring pixels. It is effective in removing salt-and-pepper noise and preserving edges.

The key difference between spatial noise reduction and temporal noise reduction is that spatial noise reduction operates on individual frames, while temporal noise reduction works across multiple frames, leveraging the temporal correlation between consecutive frames to reduce noise.

Long exposure noise reduction is a technique specifically designed to reduce noise in low-light conditions. It involves capturing multiple frames at a slow shutter speed and then combining them to create a single image with reduced noise.

Raw images allow for more precise noise reduction adjustments because they contain more information than JPEG images. This enables photographers to fine-tune the noise reduction process and achieve optimal results.

Temporal denoising is a noise reduction technique commonly used in video editing software. It analyzes multiple consecutive frames and uses temporal information to reduce noise while preserving image detail.

The primary drawback of using a high noise reduction setting is that it can lead to reduced image detail. Aggressive noise reduction algorithms may smooth out fine details and textures, resulting in a loss of image quality.

Denoising algorithms are specifically designed to reduce noise in images, particularly in high-ISO images. These algorithms use advanced mathematical techniques to identify and remove noise while preserving image detail.

The primary advantage of using a noise reduction filter in a camera is that it reduces the need for post-processing. By applying noise reduction directly in the camera, photographers can save time and effort in editing their images later.

Dark frame subtraction is a noise reduction technique commonly used in astrophotography. It involves capturing a dark frame (an image taken with the lens cap on) and subtracting it from the actual image to remove noise caused by the camera's sensor.

The primary purpose of using a noise reduction plugin in photo editing software is to remove unwanted noise from an image, resulting in a cleaner and more visually appealing result.

Long exposure noise reduction is a technique specifically designed to reduce noise in long exposure images. It involves capturing multiple frames at a slow shutter speed and then combining them to create a single image with reduced noise.

The primary advantage of using a noise reduction software program is that it reduces the need for post-processing. By applying noise reduction directly in the software, photographers can save time and effort in editing their images later.