Image Denoising is a fundamental image processing step for improving the overall quality of images. This paper is elaborating noise reduction method using compressive sensing. The conventional method consists of two phases: noise detection and noise filtering. The filtering is applied to only corrupt pixels of the noisy image. To overcome this problem, present a novel compressive sensing (CS)-based noise removing algorithm using adaptive multiple samplings and reconstruction error control. Compressive sensing is an emerging methodology in computational signal processing. Compressed sensing reconstruction achieves better image quality in terms of signal-to-noise ratio, local contrast, and contrast-to-noise ratio, compared to the classical averaging method while reducing the total amount of data required reconstructing the images.