An Image is a worth, a thousand words & in this digital age, images are everywhere. Most of the digital images contain some form of noise. The purpose of denoising is to reconstruct the original image from its noisy observation as accurately as possible. The important property of a good image denoising model is that it should completely remove noise as far as possible. Estimation of the noise level in an image is a very important parameter to improve the efficiency of denoising. This article presents different approaches used so far by the researchers for the estimation of blind noise level using the statistical and averaging method and denoising of an image. The paper also contains problems in different approaches identified by the survey. Image denoising has a very rich history beginning from the mid-70s. Patch based image modeling has achieved a great success in low-level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem.