Dataset Information

Lightweight Image Super-Resolution Based on Re-Parameterization and Self-Calibrated Convolution.

ABSTRACT: Image super-resolution technique can improve image quality by increasing image clarity, bringing a better user experience in real production scenarios. However, existing convolutional neural network methods usually have very deep network layers and a large number of parameters, which causes feature information to be lost as the network deepens, and models with a large numbers of parameters are not suitable for deploying on resource-constrained mobile devices. To address the above problems, we propose a novel lightweight image super-resolution network (RepSCN) based on re-parameterization and self-calibration convolution. Specifically, to reduce the computational cost while capturing more high-frequency details, we designed a re-parameterization distillation block (RepDB) and a self-calibrated distillation block (SCDB). They can improve the reconstruction results by aggregating the local distilled feature information under different receptive fields without introducing extra parameters. On the other hand, the positional information of the image is also crucial for super-resolution reconstruction. Nevertheless, existing lightweight SR methods mainly adopt the channel attention mechanism, which ignores the importance of positional information. Therefore, we introduce a lightweight coordinate attention mechanism (CAM) at the end of RepDB and SCDB to enhance the feature representation at both spatial and channel levels. Numerous experiments have shown that our network has better reconstruction performance with reduced parameters than other classical lightweight super-resolution models.

SUBMITTER: Zhang S

PROVIDER: S-EPMC9529453 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Lightweight Image Super-Resolution Based on Re-Parameterization and Self-Calibrated Convolution.

Zhang Sufan S Chen Xi X Huang Xingwei X

Computational intelligence and neuroscience 20220926

Image super-resolution technique can improve image quality by increasing image clarity, bringing a better user experience in real production scenarios. However, existing convolutional neural network methods usually have very deep network layers and a large number of parameters, which causes feature information to be lost as the network deepens, and models with a large numbers of parameters are not suitable for deploying on resource-constrained mobile devices. To address the above problems, we pr ...[more]

PMID: 36199958

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Project description:The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the accuracy and efficiency of the detection are still the challenges. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disease spot is relatively small. In particular, the detection will be difficult if the number of pixels of the spot is <32 × 32 in the image. In order to effectively address this problem, we present a super-resolution image enhancement and convolutional neural network-based algorithm for the detection of black rot on grape leaves. First, the original image is up-sampled and enhanced with local details using the bilinear interpolation. As a result, the number of pixels in the image increase. Then, the enhanced images are fed into the proposed YOLOv3-SPP network for detection. In the proposed network, the IOU (Intersection Over Union, IOU) in the original YOLOv3 network is replaced with GIOU (Generalized Intersection Over Union, GIOU). In addition, we also add the SPP (Spatial Pyramid Pooling, SPP) module to improve the detection performance of the network. Finally, the official pre-trained weights of YOLOv3 are used for fast convergence. The test set test_pv from the Plant Village and the test set test_orchard from the orchard field were used to evaluate the network performance. The results of test_pv show that the grape leaf black rot is detected by the YOLOv3-SPP with 95.79% detection accuracy and 94.52% detector recall, which is a 5.94% greater in terms of accuracy and 10.67% greater in terms of recall as compared to the original YOLOv3. The results of test_orchard show that the method proposed in this paper can be applied in field environment with 86.69% detection precision and 82.27% detector recall, and the accuracy and recall were improved to 94.05 and 93.26% if the images with the simple background. Therefore, the detection method proposed in this work effectively solves the detection task of small targets and improves the detection effectiveness of the grape leaf black rot.

Dataset Information

Lightweight Image Super-Resolution Based on Re-Parameterization and Self-Calibrated Convolution.

Publications

Lightweight Image Super-Resolution Based on Re-Parameterization and Self-Calibrated Convolution.

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