Compressed sensing (CS) uses undersampling at the expense of image blurring and increased noise. We have developed denoising deep learning reconstruction (dDLR) to reduce noise and regain signal-to-noise ratio (SNR) in highly undersampled (4-4.5x) CS images. Feasibility study was performed in fat-suppressed T2 and proton density knee images, by evaluating SNR and image quality (sharpness, blurring, and artifact scores). Compared to reference (no CS or dDLR), images obtained with CS had lower SNR (by 25 to 40%) and image scores due to sharpness and blurring. After processing with dDLR, SNR and image scores were restored the reference levels.