Image quality is greatly affected by noise in various applications such as machine vision and computer vision. Noisy images lead to inaccurate results as well as segmentation and enhancement errors, making denoising vital to efficient image processing. Existing denoising methods do not work effectively on both grayscale and color images. Thus an effective denoising method that works well on both image types is proposed. The proposed two-stage method involves: (i) noisy pixel detection and (ii) noisy pixel restoration. In the first stage, the conglomerate method detects the noisy pixel position; in the second stage, the corrupted value of the noisy pixel is restored using a conglomerate method. The proposed method was used to process a variety of grayscale and color images. The result analysis of image denoising was done in two stages: in the first stage, results are carried out using large standard datasets; comparison of these results with those of five other denoising methods showed that the proposed conglomerate denoising method outperformed these existing ones. The second stage of result analysis considers the state-of-the-art methods in comparison with the proposed method. The proposed method outperforms the state-of-the-art methods for images affected with noise ranging from 10% to 90%.