Image denoising has always been an important part of signal processing, especially in the digitized world of modern society. Local filters are one of the earliest methods of denoising which used only information from neighboring pixels with the idea that locality meant similarity. The real world scenes have a very wide range of luminance levels. But in the field of photography, the ordinary cameras are not capable of capturing the true dynamic range of a natural scene. The process denoising is employed in the images based on the type of noise applied to the image. The process of denoising images is more helpful in all image processing applications and in cameras. The images consists some unwanted pixels defined as noise. The noises in the images were removed based on the Low rank approximation SVD process. The LRA-SVD process identifies the noises in the images based on the dictionary formation for the effective removal of the noises in the images. The overall performance of the process were measured based on the performance metrics. Noise is added to the input images. LSV-SVD process is then employed for the noisy images inorder to remove noise from the input images. The noisy image is divided into patches. SVD denoising process is applied to the identified patches in the images. The denoised patches were then combined based on the patch grouping process. In the patch grouping process Fuzzy C means clustering algorithm is employed. In the identified patch group low rank approximation is employed for the effective optimization of the noisy pixels in the patches. Finally the patches were aggregated and the resulting images were back propagated resulting in the denoised images. The performance of the process is measured with the help of performance metric like PSNR, FSIM. The performance of the process measured indicates that the proposed approaches were more efficient compared to the existing approaches for the denoising of images.