A　microarray　can　be　easily　used　for　quantitatively　analyzing　the　expression　levels　of　DNA　genes.　Yet,　the　noises　introduced　during　the　application　will　greatly　affect　the　accuracy　of　DNA　sequence　detection.　How　to　reduce　the　noise　constitutes　a　challenging　problem　in　microarray　analysis.　Especially,　due　to　the　weak　fluorescence　response,　the　image　of　microarray　contains　difficulties　of　the　low　peak-signal-to-noise　ratio　(PSNR)　and　luminance　contrast.　To　solve　the　problem　that　the　wavelet　threshold　denoising　method　has　poor　effective　on　low　PSNR　image,　a　wavelet　denoising　approach　based　on　compression　sensing　(CS)　optimized　by　the　neural　dynamics　optimization　algorithm　(NDOA)　is　proposed,　which　preferably　solves　the　denoising　difficulties　of　noise　pollution　in　the　microarray　image.　Under　the　condition　of　Gaussian　random　observation　matrix,　the　effectiveness　of　NDOA-optimized　wavelet　denoising　based　on　CS　gets　better　work　than　the　orthogonal　matching　pursuit　and　its　improved　algorithms.　The　experimental　results　indicate　that　the　expected　wavelet　coefficients　of　the　noiseless　image　have　been　reconstructed　with　higher　quality.　When　the　compression　sampling　rate　for　microarray　image　is　0.875,　the　PSNR　of　the　NDOA-optimized　wavelet　denoising　algorithm　based　on　CS　is　increased　about　9　dB,　and　the　root　mean　squared　error　is　reduced　obviously　too,　in　comparison　with　the　wavelet　soft-threshold　denoising　method.　It　shows　that　the　NDOA-optimized　method　improves　the　performance　of　the　classical　wavelet　threshold　denoising.　©　2019　IEEE.