In the current generation, the transmission of visual information serves as an essential form of communication. Often during this transmission, the digital images are corrupted by noise, which is a perversion in data that degrades a neural network's performance. Thus, denoising plays a vital role in Image Processing and the reason for the never-ending quest for an effective denoising algorithm to remove or suppress the noise while preserving the image's essential information. This paper proposes an idea of applying the Guided Image Filtering technique on a denoised image. Guided Image Filtering is an edge-preserving smoothing technique that uses the second image's contents, which is the guidance/reference image. It considers the region's statistics in the corresponding spatial neighborhood of the reference image to compute the output pixel value. This proposed method obtains better results in terms of the quality of the image and noise removal, measured using PSNR and SSIM values.