A Survey of Image Denoising Algorithms

Abstract

Images play an important role in conveying important information but the images received after
transmission are often corrupted and deviate from the original value.

When an Image is formed various factors such as lighting spectra, source, intensity and camera
Characteristics (sensor response, lenses) affect the image. The major factor that reduces the
quality of the image is Noise. It hides the important details of images and changes value of image
pixels at key locations causing blurring and various other deformities. We have to remove noises
from the images without loss of any image information. Noise removal is the preprocessing stage
of image processing. There are many types of noises which corrupt the images. These noises are
appeared on images in different ways: at the time of acquisition due to noisy sensors, due to
faulty scanner or due to faulty digital camera, due to transmission channel errors, due to
corrupted storage media.

The image needs image denoising before it can be used in applications to obtain accurate results.
Various types of noises that create fault in image are discussed. Many image denoising
algorithms exist none of them are universal and their performance largely depends upon the type
of image and the type of noise. In this paper we will be discussing some of the image denoising
algorithms and comparing them with each other. A quantitative measure of the image denoising
algorithms is provided by the signal to noise ratio and the computation time of various
algorithms working on a provided noisy image.