Daoliang Li

Song Zhang, Yuqing Duan, Daoliang Li et al.

In underwater image enhancement (UIE), convolutional neural networks (CNN) have inherent limitations in modeling long-range dependencies and are less effective in recovering global features. While Transformers excel at modeling long-range dependencies, their quadratic computational complexity with increasing image resolution presents significant efficiency challenges. Additionally, most supervised learning methods lack effective physical model constraint, which can lead to insufficient realism and overfitting in generated images. To address these issues, we propose a physical model constraint-based underwater image enhancement framework, Mamba-UIE. Specifically, we decompose the input image into four components: underwater scene radiance, direct transmission map, backscatter transmission map, and global background light. These components are reassembled according to the revised underwater image formation model, and the reconstruction consistency constraint is applied between the reconstructed image and the original image, thereby achieving effective physical constraint on the underwater image enhancement process. To tackle the quadratic computational complexity of Transformers when handling long sequences, we introduce the Mamba-UIE network based on linear complexity state space models. By incorporating the Mamba in Convolution block, long-range dependencies are modeled at both the channel and spatial levels, while the CNN backbone is retained to recover local features and details. Extensive experiments on three public datasets demonstrate that our proposed Mamba-UIE outperforms existing state-of-the-art methods, achieving a PSNR of 27.13 and an SSIM of 0.93 on the UIEB dataset. Our method is available at https://github.com/zhangsong1213/Mamba-UIE.

Song Zhang, Daoliang Li, Ran Zhao

Majority of deep learning methods utilize vanilla convolution for enhancing underwater images. While vanilla convolution excels in capturing local features and learning the spatial hierarchical structure of images, it tends to smooth input images, which can somewhat limit feature expression and modeling. A prominent characteristic of underwater degraded images is blur, and the goal of enhancement is to make the textures and details (high-frequency features) in the images more visible. Therefore, we believe that leveraging high-frequency features can improve enhancement performance. To address this, we introduce Pixel Difference Convolution (PDC), which focuses on gradient information with significant changes in the image, thereby improving the modeling of enhanced images. We propose an underwater image enhancement network, PDCFNet, based on PDC and cross-level feature fusion. Specifically, we design a detail enhancement module based on PDC that employs parallel PDCs to capture high-frequency features, leading to better detail and texture enhancement. The designed cross-level feature fusion module performs operations such as concatenation and multiplication on features from different levels, ensuring sufficient interaction and enhancement between diverse features. Our proposed PDCFNet achieves a PSNR of 27.37 and an SSIM of 92.02 on the UIEB dataset, attaining the best performance to date. Our code is available at https://github.com/zhangsong1213/PDCFNet.

Meng Cui, Xianghu Yue, Xinyuan Qian et al.

Fish Feeding Intensity Assessment (FFIA) is crucial in industrial aquaculture management. Recent multi-modal approaches have shown promise in improving FFIA robustness and efficiency. However, these methods face significant challenges when adapting to new fish species or environments due to catastrophic forgetting and the lack of suitable datasets. To address these limitations, we first introduce AV-CIL-FFIA, a new dataset comprising 81,932 labelled audio-visual clips capturing feeding intensities across six different fish species in real aquaculture environments. Then, we pioneer audio-visual class incremental learning (CIL) for FFIA and demonstrate through benchmarking on AV-CIL-FFIA that it significantly outperforms single-modality methods. Existing CIL methods rely heavily on historical data. Exemplar-based approaches store raw samples, creating storage challenges, while exemplar-free methods avoid data storage but struggle to distinguish subtle feeding intensity variations across different fish species. To overcome these limitations, we introduce HAIL-FFIA, a novel audio-visual class-incremental learning framework that bridges this gap with a prototype-based approach that achieves exemplar-free efficiency while preserving essential knowledge through compact feature representations. Specifically, HAIL-FFIA employs hierarchical representation learning with a dual-path knowledge preservation mechanism that separates general intensity knowledge from fish-specific characteristics. Additionally, it features a dynamic modality balancing system that adaptively adjusts the importance of audio versus visual information based on feeding behaviour stages. Experimental results show that HAIL-FFIA is superior to SOTA methods on AV-CIL-FFIA, achieving higher accuracy with lower storage needs while effectively mitigating catastrophic forgetting in incremental fish species learning.