The　Transformer　architecture　has　surpassed　traditional　CNN-based　methods　in　the　field　of　learned　image　compression,　primarily　due　to　its　expansion　of　the　receptive　field.　In　learned　image　compression,　the　effective　receptive　field　is　crucial.　Although　the　Transformer　theoretically　has　an　extensive　receptive　field,　in　image　compression　models,　its　effective　receptive　field　is　much　smaller　than　the　theoretical　value,　accompanied　by　higher　computational　costs.　To　address　this　challenge,　this　paper　proposes　an　innovative　Multi-scale　Spatial　Channel　Fusion(MSCF)　mechanism　that　not　only　brings　the　effective　receptive　field　of　CNNs　on　par　with　Transformers　but　also　retains　the　low　complexity　and　high　efficiency　of　CNNs.　Additionally,　learned　image　compression　tends　to　lose　a　significant　amount　of　high-frequency　components.　To　compensate　for　this　deficiency,　we　introduce　a　High-Frequency　Enhancement(HFE)　module.　We　integrate　the　MSCF　mechanism　and　the　HFE　module　into　the　MLIC++　framework.　Experimental　results　indicate　that　our　proposed　model,　Multiscale　Feature　Extraction　and　High-Frequency　Enhancement　for　Learned　Image　Compression　(HMLIC)　achieves　a　substantial　performance　improvement　over　the　baseline　model　across　the　Kodak,　CLIC　Professional　Validation　and　Tecnick　test　datasets,　while　incurring　only　a　minimal　increase　in　model　complexity.　©　2024　IEEE.