TARGET IMAGE PROCESSING BASED ON SUPER-RESOLUTION RECONSTRUCTION AND DEEP MACHINE LEARNING ALGORITHM

In dictionary-based single-frame image reconstruction algorithms, dictionaries rely on the design of artificial shallow features and are limited in their ability to represent image features. Therefore, this paper proposes a high-accuracy reconstruction method based on deep learning feature dictionary. This algorithm first uses a deep network to learn high-resolution and lowresolution training example images with deep features; Then co-train the feature dictionary under the super dense framework of the sparse dictionary; Finally, a single low-resolution image can be input and a super-resolution reconstruction can be performed using a dictionary. From the theoretical analysis, the introduction of deep network to extract the deep-level features of the image and its use in dictionary training is more beneficial to complement the high-frequency information in the low-resolution image. Experiments show that the proposed method achieves the best results in terms of both the peak signal-to-noise ratio and the gradient energy function of the reconstructed images. This shows that compared with traditional interpolation methods and some deep learning methods, the proposed method can recover image details to a high degree while preserving the original image damage information. This proves that the subjective visual and objective evaluation indicators of the algorithm presented in this article are higher than those of the comparative algorithm.