This　paper　proposes　a　sparse　representation　layer　in　the　feature　extraction　stage　of　a　convolutional　neural　network　(CNN).　Our　goal　is　to　add　sparse　transforms　to　a　target　network　to　improve　its　performance　without　introducing　an　extra　calculation　burden.　First,　the　proposed　method　was　achieved　by　inserting　the　sparse　representation　layers　into　a　target　network＇s　shallow　layers,　and　the　network　was　trained　end-to-end　using　a　supervised　learning　algorithm.　Second,　In　the　forward　pass　the　network　captured　the　features　through　the　convolutional　layers　and　sparse　representation　layers　accomplished　with　wavelet　and　shearlet　transforms.　Thirdly,　in　the　backward　pass　the　weights　of　the　learned　kernels　of　the　network　were　updated　through　a　back-propagated　error,　while　the　sparse　representation　layers　were　fixed　and　did　not　require　updating.　The　proposed　method　was　verified　on　five　datasets　with　the　task　of　image　classification:　FOOD-101,　CIFAR10/100,　DTD,　Brodatz　and　ImageNet.　The　experimental　results　show　that　the　proposed　method　leads　to　higher　recognition　accuracy　in　image　classification,　and　the　additional　computational　cost　is　relatively　small　compared　to　the　baseline　CNN　model.　©　2020　Elsevier　B.V.