Spectral　reconstruction　from　RGB　images　has　made　significant　progress.　Previous　works　usually　utilized　the　noise-free　RGB　images　as　input　to　reconstruct　the　corresponding　hyperspectral　images　(HSIs).　However,　due　to　instrumental　limitation　or　atmospheric　interference,　it　is　inevitable　to　suffer　from　noise　(e.g.,　Gaussian　noise)　in　the　actual　image　acquisition　process,　which　further　increases　the　difficulty　of　spectral　reconstruction.　In　this　article,　we　propose　an　enhanced　channel　attention　network　(ECANet)　to　learn　a　nonlinear　mapping　from　noisy　RGB　images　to　clean　HSIs.　The　backbone　of　our　proposed　ECANet　is　stacked　with　multiple　enhanced　channel　attention　(ECA)　blocks.　The　ECA　block　is　the　dual　residual　version　of　the　channel　attention　block,　which　makes　the　network　focus　on　key　auxiliary　information　and　features　that　are　more　conducive　to　spectral　reconstruction.　For　the　case　that　the　input　RGB　images　are　disturbed　by　Gaussian　noise,　cross-layer　feature　fusion　unit　is　used　to　concatenate　the　multiple　feature　maps　at　different　depths　for　more　powerful　feature　representations.　In　addition,　we　design　a　novel　combined　loss　function　as　the　constraint　of　the　ECANet　to　achieve　more　accurate　reconstruction　result.　Experimental　results　on　two　HSI　benchmarks,　CAVE　and　NTIRE　2020,　demonstrate　that　the　effectiveness　of　our　method　in　terms　of　both　visual　and　quantitative　over　other　state-of-the-art　methods.