# ISAR Image Quality Assessment Based on Visual Attention Model
## 1 Introduction
Additionally, our method utilizes cross-channel and cross-region attention mechanisms to enhance interactions between both channels and spatial features. The Inter-Region Attention Block (IRAB), which focuses on spatial relationships, captures distant semantic relationships between locations. Given that ISAR images are formed from target scattering points and the background contains substantial silent noise, IRAB is used to extract local scattering point features. The key to ISAR image quality lies in the clarity of target imaging. Therefore, IRAB is beneficial for the quality assessment of ISAR images. The scattering intensity of targets in ISAR images is reflected across multiple channel dimensions. Therefore, the Channel Attention Block (CAB) module is utilized to aggregate and mine multi-channel information to extract target representations, which facilitates the objective evaluation of ISAR image quality. This cross-dimensional approach allows modules to synergistically improve interactions between local regions and various channels, thereby enhancing the representational capability of the algorithm. In ISAR (Inverse Synthetic Aperture Radar) images, targets appear as isolated and sparse groups of strong scatterers, which reflect their electromagnetic scattering structures. Moreover, the imaging texture effects of these targets remain relatively stable at different angles. This leads to structural and texture similarities between patches in ISAR images, which encourages us to design structural and texture constraints to improve model performance. In summary, the key research contributions and innovations of this paper include the following:  
1. In this paper, Gram-T is designed for the quality assessment of ISAR images. The Gram-T model is highly adaptable and flexible when dealing with sequence data. By transforming an image into sequence data, the Transformer encoder can effectively grab spatial relationships and global information in the image. Furthermore, computing the Gramin matrix enhances the contrast of features representing different quality levels of ISAR images, thereby strengthening the association between ISAR image features and quality levels. In this way, a strong association between features and image quality can be explored, conducive to more discriminative features. 2. We employ the Channel Attention Block (CAB) and Inter-Region Attention Block (IRAB) to enhance interactions between channels and space within features. The spatial dimension-oriented attention module captures local scattering point features, while the channel-oriented attention module captures semantic association information on channels. The CAB and IRAB modules enhance the extraction of features that characterize target clarity and image quality. 3. Structure and texture constraints are proposed to boost model performance due to the exclusive properties of ISAR images. Extensive experiments are conducted on the ISAR dataset. Compared with various network architectures and the latest ISAR image evaluation methods, our method achieves the best performance. Ablation experiments demonstrate the effectiveness of each module.