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Artefacts in high‐resolution multidimensional nuclear magnetic resonance (NMR) spectra, known as t1 noise, can significantly downgrade the spectral quality and remain a significant noise source, limiting the sensitivity of most two‐dimensional NMR experiments. In addition to highly sensitive hardware and experimental designs, data post‐processing is a relatively simple and cost‐effective method for suppressing t1 noise. In this study, histograms and quantiles were used to obtain a robust estimation of noise level. We constructed a weighted matrix to suppress the t1 noise. The weighted matrix was calculated from the logistic functions, which were adaptively computed from the spectrum. The proposed method is robust and effective in both simulations and actual experiments. Further, it can maintain the quantitative relationship of the spectrogram and is suitable for various complex peak types. [ABSTRACT FROM AUTHOR]