Underwater images suffer from color casts and low illumination due to the scattering and absorption of light as it propagates in water. These problems can interfere with underwater vision tasks, such as recognition and detection. We propose an adaptive learning attention network for underwater image enhancement based on supervised learning, named LANet, to solve these degradation issues. First, a multiscale fusion module is proposed to combine different spatial information. Second, we design a novel parallel attention module(PAM) to focus on the illuminated features and more significant color information coupled with the pixel and channel attention. Then, an adaptive learning module(ALM) can retain the shallow information and adaptively learn important feature information. Further, we utilize a multinomial loss function that is formed by mean absolute error and perceptual loss. Finally, we introduce an asynchronous training mode to promote the network’s performance of multinomial loss function. Qualitative analysis and quantitative evaluations show the excellent performance of our method on different underwater datasets. The code is available at: https://github.com/LiuShiBen/LANet .

Accurate lesion segmentation based on endoscopy images is a fundamental task for the automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually use hand-crafted features for representing endoscopy images, while feature definition and lesion segmentation are treated as two standalone tasks. Due to the possible heterogeneity between features and segmentation models, these methods often result in sub-optimal performance. Several fully convolutional networks have been recently developed to jointly perform feature learning and model training for GI Tract disease diagnosis. However, they generally ignore local spatial details of endoscopy images, as down-sampling operations (e.g., pooling and convolutional striding) may result in irreversible loss of image spatial information. To this end, we propose a multi-scale context-guided deep network (MCNet) for end-to-end lesion segmentation of endoscopy images in GI Tract, where both global and local contexts are captured as guidance for model training. Specifically, one global subnetwork is designed to extract the global structure and high-level semantic context of each input image. Then we further design two cascaded local subnetworks based on output feature maps of the global subnetwork, aiming to capture both local appearance information and relatively high-level semantic information in a multi-scale manner. Those feature maps learned by three subnetworks are further fused for the subsequent task of lesion segmentation. We have evaluated the proposed MCNet on 1,310 endoscopy images from the public EndoVis-Ab and CVC-ClinicDB datasets for abnormal segmentation and polyp segmentation, respectively. Experimental results demonstrate that MCNet achieves 74% and 85% mean intersection over union (IoU) on two datasets, respectively, outperforming several state-of-the-art approaches in automated lesion segmentation with endoscopy images of GI Tract.

With the development of deep learning, impressive progress has been made in the field of image restoration. The existing methods mainly rely on CNN and Transformer to obtain multi-scale feature information. However, these methods rarely integrate frequency domain information effectively during feature extraction, limiting their performance in image restoration. Additionally, few have combined Mamba with the Fourier domain for image restoration, which limits Mamba’s ability to perceive global degradation in the frequency domain. Therefore, we propose a new image restoration model called FMambaIR, which utilizes the complementarity between frequency and Mamba for image restoration. The core of FMambaIR is the F-Mamba block, which combines Fourier transform and Mamba for global degradation perception modeling. Specifically, F-Mamba adopts a dual branch complementary structure, including spatial Mamba branches and Fourier frequency domain global modeling. Mamba models the long-range dependencies of the entire image features, and the frequency branch utilizes Fourier to extract global degraded features from the image. Finally, we use a forward feedback network to integrate local information, which is beneficial for improving the recovery details. We comprehensively evaluate FMambaIR on several image restoration tasks, including underwater image enhancement, remote sensing image dehazing, and low-light image enhancement. The experimental results demonstrate that FMambaIR not only achieves superior performance compared to state-of-the-art methods but also significantly reduces computational complexity. Our code is available at https://github.com/mickoluan/FMambaIR.

Shadow removal, which aims to restore the illumination in shadow regions, is challenging due to the diversity of shadows in terms of location, intensity, shape, and size. Different from most multi-task methods, which design elaborate multi-branch or multi-stage structures for better shadow removal, we introduce feature decomposition to learn better feature representations. Specifically, we propose a single-stage and decoupled multi-task network (DMTN) to explicitly learn the decomposed features for shadow removal, shadow matte estimation, and shadow image reconstruction. First, we propose several coarse-to-fine semi-convolution (SMC) modules to capture features sufficient for joint learning of these three tasks. Second, we design a theoretically supported feature decoupling layer to explicitly decouple the learned features into shadow image features and shadow matte features via weight reassignment. Last, these features are converted to a target shadow-free image, affiliated shadow matte, and shadow image, supervised by multi-task joint loss functions. With multi-task collaboration, DMTN effectively recovers the illumination in shadow areas while ensuring the fidelity of non-shadow areas. Experimental results show that DMTN competes favorably with state-of-the-art multi-branch/multi-stage shadow removal methods, while maintaining the simplicity of single-stage methods. We have released our code to encourage future exploration in powerful feature representation for shadow removal https://github.com/nachifur/DMTN

Most existing dehazing networks rely on synthetic hazy-clear image pairs for training, and thus fail to work well in real-world scenes. In this paper, we deduce a reformulated atmospheric scattering model for a hazy image and propose a novel lightweight two-branch dehazing network. In the model, we use a Transformation Map to represent the dehazing transformation and use a Compensation Map to represent variable illumination compensation. Based on this model, we design a T wo- B ranch N etwork (TBN) to jointly estimate the Transformation Map and Compensation Map. Our TBN is designed with a shared Feature Extraction Module and two Adaptive Weight Modules. The Feature Extraction Module is used to extract shared features from hazy images. The two Adaptive Weight Modules generate two groups of adaptive weighted features for the Transformation Map and Compensation Map, respectively. This design allows for a targeted conversion of features to the Transformation Map and Compensation Map. To further improve the dehazing performance in the real-world, we propose a semi-supervised learning strategy for TBN. Specifically, by performing supervised pre-training based on synthetic image pairs, we propose a Self-Enhancement method to generate pseudo-labels, and then further train our TBN with the pseudo-labels in a semi-supervised way. Extensive experiments demonstrate that the model-based TBN outperforms the state-of-the-art methods on various real-world datasets.

Existing RGB-Thermal trackers usually treat intra-modal feature extraction and inter-modal feature fusion as two separate processes, therefore the mutual promotion of extraction and fusion is neglected. Then, the complementary advantages of RGB-T fusion are not fully exploited, and the independent feature extraction is not adaptive to modal quality fluctuation during tracking. To address the limitations, we design a joint-modality query fusion network, in which the intra-modal feature extraction and the inter-modal fusion are coupled together and promote each other via joint-modality queries. The queries are initialized based on the multimodal features of the current frame, making the subsequent fusion adaptive to modal quality fluctuation during tracking. Then the joint-modality query fusion (JQF) utilizes the queries to interact with RGB-T features, allowing the intra-modal enhancement and the inter-modal interactions to be unified for mutual promotion. In this way, JQF can distinguish and enhance the complementary modality features, while filtering out redundant information. For real-time tracking, we propose regional cross-attention for cross-modal interactions to reduce computational cost. Our end-to-end tracker sets a new state-of-the-art performance on multiple RGBT tracking benchmarks including LasHeR, VTUAV, RGBT234 and GTOT, while running at a real-time speed.