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The task of classifying small objects is still challenging for current deep learning classification models [such as convolutional neural networks (CNNs) and vision transformers (ViTs)]. We believe that these algorithms are not designed specifically for small targets, so their feature extraction abilities for small targets are insufficient. To improve the classification capabilities of CNN-based and ViT-based classification models for small objects, two multidomain feature fusion (MDFF) frameworks are proposed to increase the amount of feature information derived from images and they are called MDFF-ConvMixer and MDFF-ViT. Compared with the basic model, the uniquely added design includes frequency domain feature extraction and MDFF processes. In the frequency domain feature extraction part, the input image is first transformed into a frequency domain form through discrete cosine transform (DCT) transformation and then a three-dimensional matrix containing the frequency domain information is obtained via channel splicing and reshaping. In the MDFF part, MDFF-ConvMixer splices the spatial and frequency domain features by channel, whereas MDFF-ViT uses a cross-attention mechanism to fuse the spatial and frequency domain features. When targeting small target classification tasks, these two frameworks obviously improve the utilized classification algorithm. On the DOTA dataset and the CIFAR10 dataset with two downsampling operations, the accuracies of MDFF-ConvMixer relative to ConvMixer increase from 87.82% and 62.14% to 90.14% and 66.00%, respectively, and the accuracies of MDFF-ViT relative to the ViT increase from 79.22% and 36.2% to 88.15% and 59.23%, respectively.

Most of the laser interfered image quality assessment algorithms need to know the reference images or partial information of reference images. However, in practical application, the reference image or its related informa-tion is difficult to obtain, which makes the application scenario of laser interference image quality evaluation algorithm is greatly limited. To solve this problem, this paper starts with the prediction processing of the ob-scured information, and improves the Markov random field estimation algorithm (MRF) to realize the real-time estimation of the obscured area information. Then, proposes a non-reference image quality assessment method based on occlusion area information estimation and natural scene statistics(IENSS), which analyzes the statistical characteristics of laser interfered images in natural scenes. The model is trained by machine learning. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method.

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