Among photon counting detector (PCD)-based technologies, the K-edge subtraction (KES) method has a very high material decomposition efficiency. Yet, since the increase in noise in the X-ray image to which the KES method is applied is inevitable, research on image quality improvement is essential. Here, we modeled a block-matching and 3D filtering (BM3D) algorithm and applied it to PCD-based X-ray images with the improved KES (IKES) method. For PCD modeling, Monte Carlo simulation was used, and a phantom composed of iodine substances with different concentrations was designed. The IKES method was modeled by adding a log term to KES, and the X-ray image used for subtraction was obtained by applying the 3.0 keV range based on the K-edge region of iodine. As a result, the IKES image using the BM3D algorithm showed the lowest normalized noise power spectrum value. In addition, we confirmed that the contrast-to-noise ratio and no-reference-based evaluation results when the BM3D algorithm was applied to the IKES image were improved by 29.36 % and 20.56 %, respectively, compared to the noisy image. In conclusion, we demonstrated that the IKES imaging technique using a PCD-based detector and the BM3D algorithm fusion technique were very efficient for X-ray imaging.