Since light is scattered and absorbed by water, underwater images have inherent degradation (e.g. hazing, color shift), consequently impeding the development of remotely operated vehicles. Towards this end, we propose a novel method, referred to as Best o f Bo th Worlds (Boths). With parameters of only 0.0064M, Boths can be considered a super lightweight neural network for underwater image enhancement. On the whole, it has three levels: Structure and Detail Features; Pixel and Channel Dimensions; High and Low Frequency Information. Each of these three levels represents ”Best of Both Worlds”. Initially, by interacting with structure and detail features, Boths can focus on these two aspects at the same time. Further, our network can simultaneously consider channel and pixel dimensions through 3D attention learning, which is more similar to human visual perception. Lastly, the proposed model can focus on high and low frequency information, through a novel loss function based on the wavelet transforms. Upon subsequent analysis and evaluation, Boths has shown superior performance compared with state-of-the-art methods. Our models and datasets are publicly available at: https://github.com/perseveranceLX/Boths.

Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques. Despite this, the academic realm currently lacks extensive, realistic datasets and deep learning strategies specifically crafted for apricot trees. This study introduces ATZD01, a publicly accessible dataset encompassing 11 categories of apricot tree pests and diseases, meticulously compiled under genuine field conditions. Furthermore, we introduce an innovative detection algorithm founded on convolutional neural networks, specifically devised for the management of apricot tree pests and diseases. To enhance the accuracy of detection, we have developed a novel object detection framework, APNet, alongside a dedicated module, the Adaptive Thresholding Algorithm (ATA), tailored for the detection of apricot tree afflictions. Experimental evaluations reveal that our proposed algorithm attains an accuracy rate of 87.1% on ATZD01, surpassing the performance of all other leading algorithms tested, thereby affirming the effectiveness of our dataset and model. The code and dataset will be made available at https://github.com/meanlang/ATZD01 .

Texture classification is one of the essential problems in computer vision. Due to the powerful feature extraction ability, convolutional neural network (CNN) based texture classification methods have attracted extensive attention in recent years. However, there are still some challenges, such as the extraction of multi-level texture features and their relationships. To address these problems, this letter proposes the wavelet multi-level attention capsule network (WMACapsNet), which integrates multi-scale wavelet decomposition and multi-level attention blocks into the capsule network. Specifically, multi-scale spectral features in frequency domain are extracted by multi-level wavelet transform; and then the self-attention block explores the dependencies of capsule features within each scale; finally, the cross-attention block refines capsule features and their relationships with attention mechanism across different scales. The proposed WMACapsNet provides an efficient way to explore spatial domain features, frequency domain features and their dependencies, useful for most texture classification tasks. Experimental results on several texture datasets show that the proposed WMACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness.

With the growing demand for information security, finger-vein recognition has become widespread. However, the robustness of the recognition process becomes a major problem. When identifying unseen categories with traditional finger-vein recognition systems, a few issues remain, such as recognition interference and low efficiency. This paper proposes a Deep Generalized Label Finger-Vein (DGLFV) model to extract feature maps and achieve high-accuracy recognition. The largest rectangular finger-vein region is extracted through image semantic segmentation and the advanced bidirectional traversing and center diffusion method for the known categories. Then we generalize all the unseen categories actively as Class $C+1$ to reduce interference from unregistered users. Furthermore, an adaptive threshold acquisition algorithm is proposed for Label Receiver Operating Characteristic (LROC), so that the procedures of classification, recognition, and verification are unified. Apart from Shandong University Homologous Multi-modal Traits (SDUMLA-HMT), we have conducted additional experiments on our self-built database, Finger Veins of Signal and Information Processing Laboratory (FV-SIPL). The recognition accuracy of the approach proposed in this paper has reached 99.25% and 99.08% testing on FV-SIPL and SDUMLA-HMT, with a low error rate at 1.481% and 2.228% and little time consumption of 0.157s for a single image, which is better than most state-of-the-art finger-vein recognition methods.

Light can be absorbed and scattered when traveling through water, which results in underwater optical images suffering from blurring and color distortion. To improve the visual quality of underwater optical images, we propose a novel, to the best of our knowledge, image sharpening method. We utilize the relative total variation model to decompose images into structure and texture layers in a novel manner. On those two layers, the red-blue dark channel prior (RBDCP) and detail lifting algorithms are proposed, respectively. The RBDCP model calculates background light based on brightness, gradient discrimination, and hue judgment, which then generates transmission maps using red-blue channel attenuation characteristics. The linear combination of the Gaussian kernel and binary mask is employed in the proposed detail lifting algorithm. Furthermore, we combine the layers of restoration structure and enhancement texture for image sharpening, inspired by the concept of fusion. Our methodology has rich texture information and is effective in color correction and atomization removal through RBDCP. Extensive experimental results indicate that the proposed method effectively balances image hue, saturation, and clarity.