Deep convolutional neural network (CNN) has made impressive achievements in the field of image restoration. However, most of deep CNN-based models have limited capability of utilizing the hierarchical features and these features are often treated equally, thus restricting the restoration performance. To address this issue, the present work proposes a novel memory-based latent attention network (MLANet) aiming to effectively restore a high-quality image from a corresponding low-quality one. The key idea of this work is to employ a memory-based latent attention block (MLAB), which is stacked in MLANet and makes better use of global and local features through the network. Specifically, the MLAB contains a main branch and a latent branch. The former is used to extract local multi-level features, and the latter preserves global information by the structure within a latent design. Furthermore, a multi-kernel attention module is incorporated into the latent branch to adaptively learn more effective features with mixed attention. To validate the effectiveness and generalization ability, MLANet is evaluated on three representative image restoration tasks: image super-resolution, image denoising, and image compression artifact reduction. Experimental results show that MLANet performs better than the state-of-the-art methods on all the tasks. INDEX TERMS Image restoration, deep learning, deep memory-based network, latent attention block.
Vaginitis is one of the commonly encountered diseases of female reproductive tract infections. The clinical diagnosis mainly relies on manual observation under a microscope. There has been some investigation on the classification of vaginitis diseases based on computer-aided diagnosis to reduce the workload of clinical laboratory staff. However, the studies only using RGB images limit the development of vaginitis diagnosis. Through multi-spectral technology, we propose a vaginitis classification algorithm based on multi-spectral image feature layer fusion. Compared with the traditional RGB image, our approach improves the classification accuracy by 11.39%, precision by 15.82%, and recall by 27.25%. Meanwhile, we prove that the level of influence of each spectrum on the disease is distinctive, and the subdivided spectral image is more conducive to the image analysis of vaginitis disease.
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