Abstract: The traditional block-matching 3D collaborative filtering method has achieved good results in digital signal denoising.However, its block-matching and grouping results are easily affected by the energy difference between blocks, in which case the efficiency of parameter selection is not high.Therefore, further improvement of the denoising effect is warranted.In this work, a block-matching 3D collaborative filtering method is proposed based on block-energy normalization.Key steps of the method are as follows.Firstly, the block data were processed using soft/hard threshold filtering and block-energy normalization in the 2D transformation (1D transformation in the row (column) direction was performed first, followed by 1D transformation in the column (row) direction).Then block-matching and grouping in the 2D transformation domain, and 1D transformation in the block sequence direction were carried out, thereby achieving the 3D transformation of the block data.Soft/hard threshold filtering in the 3D transformation domain was performed, followed by 3D inverse transformation of the block data, and block aggregation with preservation of the energy relationship between block data.Theoretical analysis, model data testing, and application to actual data all showed that the proposed method can eliminate the influence of the energy difference between blocks, thereby improving the matching accuracy and computational efficiency across similar blocks, and ultimately improving the denoising effect, which can be widely used in denoising.