# ISAR Image Quality Assessment Based on Visual Attention Model
## 1 Introduction  
The development of radar imaging technology has led to an increased application of ISAR imaging in target recognition [1]. However, the quality of ISAR images significantly impacts the detection and recognition of targets [2]. Consequently, assessing ISAR image quality becomes a fundamental task for ISAR image applications. The field of general image quality assessment [3, 4] has advanced significantly; however, assessing the quality of ISAR images [5] remains a considerable challenge. Traditional methods for ISAR quality assessment [6, 7] typically rely on manually defined features, such as mean values, equivalent number of looks, and contrast. These approaches can be labor-intensive and prone to subjective evaluation errors. Additionally, traditionalassessment methods primarily focus on image resolution, noise, and contrast, with limited attention given to other critical image features such as detail retention and target contour clarity. These limitations represent a significant bottleneck in the quality evaluation of ISAR images, which hinders their broader application. To enable a more comprehensive and objective evaluation of ISAR images, further research and development in ISAR image quality assessment is essential. In recent years, general image quality assessment, as a hot issue in the image content-aware understanding area, has attracted much attention from academia and industry. The deep learning methods for ISAR image quality assessment imitate human vision systems and automatically predict the quality of ISAR images [8]. Deep models extract visual information from various receptive fields by neural layers, which provides ISAR images with a tremendous representation ability. The adoption of deep learning methods contributes to the collection of comprehensive and objective features during the training phase, thereby facilitating a more accurate assessment of ISAR images [9]. Some research methods evaluate ISAR image quality by local features extracted with a few convolution layers [8; 9; 10; 11], which cannot capture interactions across regions and channels in the image and hence cannot evaluate the image from a global level. The Transformer model is able to capture global information from the image [12]. Through the self-attention mechanism [13; 14; 15], the transformer encoder is capable of learning complex features and relationships in ISAR images, thereby facilitating a more comprehensive assessment of the quality of ISAR images. However, despite the increase in the number of parameters in previous works [3; 16], which employ transformers to extract features to evaluate image quality, the relation between the extracted features and the quality score is not carefully considered, resulting in a comparatively modest feature discrimination. In addition, existing deep learning-based methods neglect distinctive characteristics of ISAR images. Generally, they adopt MSE loss, which is common in generic image quality evaluation areas. However, no corresponding loss function has been designed for ISAR images, which poses an obstacle to the optimization of the ISAR image quality evaluation model. We have designed the Gramin Transformer (Gram-T) to obtain features that are closely associated with image quality scores. Our approach involves computing the Gramin matrix of features to generate score tokens through a single attention layer. We propose a new network that incorporates the Gramin Transformer along with two attention mechanism blocks. Specifically, we apply the Gramin matrix to features extracted from transformer encoders to derive score tokens that are tightly linked to these features. This allows us to explore image quality information during the feature extraction process rather than just at the final fully connected layer, effectively reducing the loss of critical information. As a result, we are able to obtain more discriminative features and strengthen their relevance to image quality.