A Survey of Image Denoising Algorithms

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.