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A statistical noise compensation algorithm is proposed for cochlear implant processing to improve cochlear implant patients' speech performance in noise. With the well-known environmental model for speech in additive noise, the MMSE (minimum mean square error) estimation of clean speech signals was derived according to the noisy speech observation based on a linear approximation of the original nonlinear environmental model. Words-in-sentences recognition by four cochlear implant subjects was tested under different noisy listening conditions (steady white noise and 6-talker speech babble at +15, +10, +5, and 0 dB SNR) with and without the noise compensation algorithm. For steady white noise, a mean improvement of 36% correct of sentence recognition scores was obtained across the SNR levels when the noise compensation algorithm was applied to cochlear implant processing. However, the amount of improvement was highly dependent on the SNR levels with the speech babble noise. The improvement was gradually increased from 7% to 32% correct when the SNR levels increased from 0 dB to 15 dB. The results suggest that cochlear implant patients may significantly benefit from the proposed noise compensation algorithm in noisy listening.