现有的针对真实失真的无参考图像质量评价算法提取的特征对自然场景图像质量的表征能力较差，限制了其评估准确性和泛化能力。针对该问题，本文提出了一个基于多尺度特征逐层融合的深度神经网络（MsFF-Net）。首先，利用预训练的深度神经网络ResNet-50提取图像多尺度特征。然后，提出了一种特征融合模块，通过逐层递进融合相邻尺度特征，获得更准确表征图像质量的多尺度融合特征。接着，从多尺度融合特征提取低维特征，得到多粒度的图像质量感知特征。最后，利用由最高层特征自适应生成的全连接神经网络，对低维特征进行回归，得到自然场景图像的质量预测。仿真结果表明，MsFF-Net在真实失真数据库上的性能优于目前大多数方法，而且在合成失真数据库也取得了出色的评价性能。

The existing deep network-based no-reference image quality assessment algorithms for authentic distortions have poor representation ability for the quality of natural scene images, limiting their evaluation accuracy and generalization ability. To solve this problem, this paper proposes a deep neural network for no-reference image quality assessment to fuse multi-scale features layer-by-layer (MsFF-net). The pre-trained ResNet50 is first used to extract multi-scale features of the image. Then, a multi-scale features fusion module is proposed, which gradually fuses adjacent-scale features layer by layer to obtain multi-scale fused features that can accurately represent the image quality. The low-dimensional features are further extracted from the multi-scale fused features to obtain multi-granularity image quality perception features. Finally, regression is performed on the low-dimensional features by using a fully connected network which is adaptively generated by the highest-level features. The simulation results show that MSFF-net outperforms most of the current methods on authentic distortions databases, and it achieves competing performances on synthetic distortions databases.