Recently, neural image compression has made significant progress in reducing rate-distortion and has received widespread attention. However, existing methods focus more on perfecting entropy models yet overlook the ability of their encoder networks to extract non-linear features of images, which can promote compression performance. In this paper, we design a learning-based asymmetric image compression network to enhance the feature representation capability for improved compression quality. Firstly, we propose a high-preserving information block (HPIB) consisting of a high-frequency filtering module (HFM) and a feature modulation module (FMM) to fully utilize the different frequency information in images. Secondly, we progressively use the HPIB layer to design a high-performance encoder network for high-fidelity feature extraction. Results from extensive experiments demonstrate that our network performs superior to the prior art in terms of both PSNR and MS-SSIM metrics and achieves 3.91% and 8.88 % BD-rate over VVC on the Kodak and CLIC datasets, respectively.