In this paper, a novel method is proposed to determine noisy blocks of an image. Three different threshold values for noisy/clean classification of the blocks of any image are determined by applying the common vector approach to the reference data set consisting of the clean samples of that image. The noise addressed in this paper is Gaussian noise with zero mean. By making a block-based noisy/clean classification of any image, it is possible to expose only the noisy blocks to the denoising process, rather than the entire image to denoising. When the first threshold value (Threshold 1) is considered for all peak signal-to-noise ratio (PSNR) values, more than 94% classification results are obtained for all 8 x 8 block-sized test images except images with 28-31 dB PSNR and more than 98.8% classification results are obtained for all 12 x 12 and 16 x 16 block-sized test images except images with 29-31 dB PSNR. Finally, popular image denoising algorithms are applied to the noisy images for comparison. It is observed that the PSNR values of noisy images are appreciably increased after the process.