An adaptive scaled mean square error (SMSE) filter using a Hopfield neural-network-based algorithm is presented. We show the development of the original SMSE filter from the minimum mean square error (MMSE) filter and the parametric mean square error (PMSE) filter, both of which suffer from the oversmooth phenomena. The SMSE filter is more efficient than the PMSE filter in terms of noise removal as it does not take into account all the correlation factors used for image enhancement. To further improve the performance of the SMSE filter, an adaptive approach is introduced. The adaptive SMSE filter uses a mask operation technique. A user-defined mask is moved across the image and the filtering parameters are computed based on the local image statistics of the region below the mask. The original and the adaptive SMSE filters are implemented using a Hopfield neural-network-based algorithm. A number of experiments were performed to test the filter characteristics.