  During the acquisition of an image from its source, noise always becomes an
integral part of it. Various algorithms have been used in past to denoise the
images. Image denoising still has scope for improvement. Visual information
transmitted in the form of digital images has become a considerable method of
communication in the modern age, but the image obtained after the transmission
is often corrupted due to noise. In this paper, we review the existing
denoising algorithms such as filtering approach and wavelets based approach and
then perform their comparative study with bilateral filters. We use different
noise models to describe additive and multiplicative noise in an image. Based
on the samples of degraded pixel neighbourhoods as inputs, the output of an
efficient filtering approach has shown a better image denoising performance.
This yields promising qualitative and quantitative results of the degraded
noisy images in terms of Peak Signal to Noise Ratio, Mean Square Error and
Universal Quality Identifier.
