Underwater images suffer from severe distortion, which degrades the accuracy of object detection performed in an underwater environment. Existing underwater image enhancement algorithms focus on the restoration of contrast and scene reflection. In practice, the enhanced images may not benefit the effectiveness of detection and even lead to a severe performance drop. In this paper, we propose an object-guided twin adversarial contrastive learning based underwater enhancement method to achieve both visual-friendly and task-orientated enhancement. Concretely, we first develop a bilateral constrained closed-loop adversarial enhancement module, which eases the requirement of paired data with the unsupervised manner and preserves more informative features by coupling with the twin inverse mapping. In addition, to confer the restored images with a more realistic appearance, we also adopt the contrastive cues in the training phase. To narrow the gap between visually-oriented and detection-favorable target images, a task-aware feedback module is embedded in the enhancement process, where the coherent gradient information of the detector is incorporated to guide the enhancement towards the detection-pleasing direction. To validate the performance, we allocate a series of prolific detectors into our framework. Extensive experiments demonstrate that the enhanced results of our method show remarkable amelioration in visual quality, the accuracy of different detectors conducted on our enhanced images has been promoted notably. Moreover, we also conduct a study on semantic segmentation to illustrate how object guidance improves high-level tasks. Code and models are available at https://github.com/Jzy2017/TACL

Image fusion integrates a series of images acquired from different sensors, e.g. , infrared and visible, outputting an image with richer information than either one. Traditional and recent deep-based methods have difficulties in preserving prominent structures and recovering vital textural details for practical applications. In this article, we propose a deep network for infrared and visible image fusion cascading a feature learning module with a fusion learning mechanism. Firstly, we apply a coarse-to-fine deep architecture to learn multi-scale features for multi-modal images, which enables discovering prominent common structures for later fusion operations. The proposed feature learning module requires no well-aligned image pairs for training. Compared with the existing learning-based methods, the proposed feature learning module can ensemble numerous examples from respective modals for training, increasing the ability of feature representation. Secondly, we design an edge-guided attention mechanism upon the multi-scale features to guide the fusion focusing on common structures, thus recovering details while attenuating noise. Moreover, we provide a new aligned infrared and visible image fusion dataset, RealStreet, collected in various practical scenarios for comprehensive evaluation. Extensive experiments on two benchmarks, TNO and RealStreet, demonstrate the superiority of the proposed method over the state-of-the-art in terms of both visual inspection and objective analysis on six evaluation metrics. We also conduct the experiments on the FLIR and NIR datasets, containing foggy weather and poor light conditions, to verify the generalization and robustness of the proposed method.

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. A list of important papers discussed in this survey, corresponding codes, and additional resources on BLOs are publicly available at: https://github.com/vis-opt-group/BLO .

Due to the refraction and absorption of light by water, underwater images usually suffer from severe degradation, such as color cast, hazy blur, and low visibility, which would degrade the effectiveness of marine applications equipped on autonomous underwater vehicles. To eliminate the degradation of underwater images, we propose a target oriented perceptual adversarial fusion network, dubbed TOPAL. Concretely, we consider the degradation factors of underwater images in terms of turbidity and chromatism. And according to the degradation issues, we first develop a multi-scale dense boosted module to strengthen the visual contrast and a deep aesthetic render module to perform the color correction, respectively. After that, we employ the dual channel-wise attention module and guide the adaptive fusion of latent features, in which both diverse details and credible appearance are integrated. To bridge the gap between synthetic and real-world images, a global-local adversarial mechanism is introduced in the reconstruction. Besides, perceptual information is also embedded into the process to assist the understanding of scenery content. To evaluate the performance of TOPAL, we conduct extensive experiments on several benchmarks and make comparisons among state-of-the-art methods. Quantitative and qualitative results demonstrate that our TOPAL improves the quality of underwater images greatly and achieves superior performance than others.