Image Denoising has been a well studied problem in the field of image processing. Images are often received in defective conditions due to poor scanning and transmitting devices. Consequently, it creates problems for the subsequent process to read and understand such images. Removing noise from the original signal is still a challenging problem for researchers because noise removal introduces artifacts and causes blurring of the images. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper deals with using discrete wavelet transform derived features used for digital image texture analysis to denoise an image even in the presence of very high ratio of noise. Image Denoising is devised as a regression problem between the noise and signals, therefore, Wavelets appear to be a suitable tool for this task, because they allow analysis of images at various levels of resolution.