Background:　Electrocardiogram　(ECG)　is　crucial　in　diagnosing　and　preventing　heart　diseases.　However,　its　efficacy　is　compromised　by　the　interference　of　the　external　environment,　leading　to　potential　misdiagnoses.　Thus,　it　is　crucial　to　remove　the　noise　in　ECGs.　Recently,　deep-learning　based　ECGs　denoising　approaches　have　achieved　impressive　performance,　however,　they　only　considered　the　time-domain　information　of　ECGs.　Methods:　In　this　work,　we　propose　a　Frequency　Information　Enhanced　Half　Instance　Normalization　Network　(FIEHINet)　which　integrates　knowledge　of　both　time　and　frequency　domains　into　a　deep-learning　model　for　ECG　signal　denoising.　Two　branches　are　employed　to　extract　time　and　frequency　features　for　noise　eliminating,　respectively.　Then　the　ECG　signals　are　reconstructed　based　on　the　fused　features.　Furthermore,　masked　signal　training　is　introduced　to　improve　the　generalization　ability.　Results:　In　order　to　evaluate　the　proposed　method,　ECGs　used　are　chosen　from　five　different　databases.　The　proposed　method　for　ECG　signal　denoising　achieved　Sum　of　Squared　Distances　scores　of　3.95　+/-　7.04,　2.04　+/-　3.20,　and　0.998　+/-　1.579　for　three　kinds　of　noise　intensities.　Meanwhile,　the　classification　experimental　results　of　the　processed　dataset　with　the　proposed　method　are　3.8　%　higher　in　F1　score　than　the　original　dataset.　Conclusion:　A　model　for　removing　mixed　noises　is　successfully　developed　and　tested.　Significance:　This　study　presents　an　ECG　denoising　technique　based　on　Half　Instance　Normalization,　time-　-frequency　information,　and　masked　signal　training,　which　can　improve　ECG　interpretation　and　potentially　reduce　misdiagnoses　in　clinical　practice.