ISAR Image Quality Assessment Based on Visual Attention Model

The quality of ISAR (Inverse Synthetic Aperture Radar) images has a significant impact on the detection and recognition of targets. Therefore, ISAR image quality assessment is a fundamental prerequisite and primary link in the utilization of ISAR images. Previous ISAR image quality assessment methods typically extract hand-crafted features or use simple multi-layer networks to extract local features. Hand-crafted features and local features from networks usually lack the global information of ISAR images. Furthermore, most deep neural networks obtain feature representations by abridging the prediction quality score and the ground truth, neglecting to explore the strong correlations between features and quality scores in the stage of feature extraction. This study proposes a Gramin Transformer to explore the similarity and diversity of features extracted from different images, thus obtaining features containing quality-related information. The Gramin matrix of features is computed to obtain the score token through the self-attention layer. It prompts the network to learn more discriminative features, which are closely associated with quality scores. Despite the Transformer architecture’s ability to extract global information, the Channel Attention Block (CAB) can capture complementary information from different channels in an image, aggregating and mining information from these channels to provide a more comprehensive evaluation of ISAR images. ISAR images are formed from target scattering points with a background containing substantial silent noise, and the Inter-Region Attention Block (IRAB) is utilized to extract local scattering point features, which decide the clarity of target. In addition, extensive experiments are conducted on the ISAR image dataset (including space stations, ships, aircraft, etc.). The evaluation results of our method on the dataset are significantly superior to those of traditional feature extraction methods and existing image quality assessment methods.