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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the two problems that most existing deep network-based fusion approaches have complex network architecture with high computation cost, and they fail to adequately consider the intrinsic characteristics of multi-modal images which results in insufficient information interaction of cross-modality features, a re-parameterized infrared and visible image fusion network based on coordinate attention is proposed. In this network, re-parameterization technique is introduced and combined with residual learning to perform feature extraction for computing efficiency and satisfying fusion quality. Moreover, to improve the interactivity of cross-modality features and fully utilize multi-modal image information, a coordinate attention-based fusion module is devised to yield fused feature. Considering the information loss during extracting process, a fused feature enhance module which leverages the preceding cross-modality features to implement feature compensation is further developed. Extensive experiments demonstrate that the proposed method not only has lower computational cost, but also achieves the improvement of multiple objective evaluation metrics while ensuring good visual effects. [ABSTRACT FROM AUTHOR]

Various types of crop diseases have posed a serious threat to the high-quality development of crops in agricultural production. A timely identification of disease types can greatly contribute to the prevention and control of crop diseases. However, the convolutional neural networks (CNN) recognition of crop leaf diseases cannot fully meet the large-scale production in recent years, due to the numerous parameters, high computational complexity, and poor real-time performance. There is a high demand to improve the accuracy of lightweight CNN models under the relatively complex background of crop leaf images in natural environments. In this study, a lightweight re parameterized leaf diseases identification network (RLDNet) was proposed to identify crop leaf diseases. Firstly, reparameterization was introduced into the MobileNetV2. The inverted residual blocks were reparameterized to construct the reparameterized inverse residual (RIR) block for the high inference speed. A multi-branch architecture was adopted during the training phase. The diversity of the feature space was enriched to improve the representation learning of the network. The reparameterization was used to equivalently convert the multi-branch architecture into a single path structure for the inference after training. The output of the transformed inference model was the same as the original multi-branch. The parameter identity transformation was achieved to improve the inference speed of the model with high recognition accuracy. There was a decrease in the number of output channels and the stacking times of core modules. A shallow and narrow network structure was obtained to enhance the extraction of shallow features, such as the disease colors and textures, indicating the reduced number of model parameters. Secondly, a lightweight ultra lightweight subspace attention module attention mechanism (ULSAM) was added to the last two RIR blocks of the model. Leaf disease characteristics were combined to more effectively distinguish between backgrounds and prospects for better target classification. The interference was reduced in the disease areas from the complex backgrounds. Finally, the DepthShrinker pruning was utilized to learn the importance of the activation function in the model. The redundant parameters were removed to further reduce the space occupation. The lossless lightweight and high accuracy of the model were achieved using knowledge distillation. The recognition accuracy of 99.53% was achieved in the RLDNet on the PlantVillage dataset, whereas 98.49% on the self-built leaf disease dataset with a parameter size of 0.65 M. The inference time for a single-leaf disease image was 2.51 ms. RLDNet shared a performance similar to the Transformer-based models (such as MobileVit-S) and CNN-based models (such as ResNet18) on both datasets, with a significant reduction in parameter, respectively, compared with MobileVitS and ResNet18. The higher recognition accuracy and lighter weight were obtained in the RLDNet, compared with the lightweight models, such as MobileNetV3 and ShuffleNetV2. The improved model can be expected to effectively identify the leaf diseases in the complex backgrounds, with less parameter memory and faster inference speed. The findings can also be applied in the practical production and application of smart agriculture. [ABSTRACT FROM AUTHOR]

With the supervised deep learning algorithms, it is prone to generate artifacts when restoring the blurred solar speckle images taken by Yunnan Observatories, and it has a long training time and over-reliance on reference images, this paper proposed a weakly supervised method based on structural reparameterization combined with multi-branch module to reconstruct solar speckle images. First, deblurring model combined single-scale and multi-scale network to design, with constructing multi-branch modules to extract features of different scales, enhance detailed information, and reduce the generation of artifacts; second, each branch structure re-parameterized to make the reuse of structure parameters runs through the entire feature extraction process; after that, the deblurring model embedded in the weakly supervised training, the blurred image assorted firstly, then the degradation model used to learn different levels of degradation. Constituted paired dataset of corresponding levels, and the deblurring model used to inversely degenerate the dataset to reconstruct solar speckle images. Experimental results show that compared with the existing deblurring method, the proposed method has higher model training efficiency and less dependence on reference images, which can meet the high-resolution reconstruction requirements of solar speckle images. [ABSTRACT FROM AUTHOR]

Copyright of Forest Research is the property of Forest Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)