Although wavelet shrinkage is an effective image denoising method, it tends to over-discard the image signal energy and, thus, blur the edges of the produced image. Shrinking the wavelet coefficients of all subbands indifferently is inappropriate because denoising involves not only removing high-frequency signals but also preserving image information to the greatest possible extent. To fulfill these requirements, this study presents an intelligent fuzzy inference system (FIS) learning-based thresholding strategy. First, we propose a principal directional components analysis (PDCA) method for capturing the dominant contours of an image. Along with the principal directions, the directional wavelet transform is used to provide efficient representation of the image. In addition, adaptive directional wavelet packet (WP) decomposition is used to generate the optimal WP tree. Each subband of the WP tree is denoised separately by one of the following methods: total variation denoising, soft shrinkage, and linear interpolation shrinkage. Based on the subband level and diagonality, FIS learning is used to appropriately adjust the subband threshold. Finally, individual estimates are weighted averaged to produce the denoised image. Experimental results show that compared with other denoising methods, our method not only significantly removes heavy noise, preserving more structural edge information, but also provides better peak signal-to-noise ratio and structural similarity index performances.