The presence of environmental additive noise in the vicinity of the user typically degrades the speech intelligibility of speech processing applications. This intelligibility loss can be compensated by properly preprocessing the speech signal prior to play-out, often referred to as near-end speech enhancement. Although the majority of such algorithms focus primarily on the presence of additive noise, reverberation can also severely degrade intelligibility. In this paper we investigate how late reverberation and additive noise can be jointly taken into account in the near-end speech enhancement process. For this effort we use a recently presented approximation of the speech intelligibility index under a power constraint, which we optimize for speech degraded by both additive noise and late reverberation. The algorithm results in time–frequency dependent amplification factors that depend on both the additive noise power spectral density as well as the late reverberation energy. These amplification factors redistribute speech energy across frequency and perform a dynamic range compression. Experimental results using both instrumental intelligibility measures as well as intelligibility listening tests show that the proposed approach improves speech intelligibility over state-of-the-art reference methods when speech signals are degraded simultaneously by additive noise and reverberation. Speech intelligibility improvements in the order of 20% are observed.