Underwater imaging in turbid environments presents significant challenges for industrial applications due to optical distortions that severely degrade image quality. This article addresses the fundamental information deficit in traditional single-image restoration methods by introducing the first comprehensive Light Field Underwater Image Dataset, comprising 1356 images captured across diverse environments with 1820× 720× 14× 14× 3 resolution. We develop a novel physics-based image formation model that extends scattering principles to the 4-D light field domain, incorporating water attenuation coefficients and scattering properties while accounting for light field camera characteristics. Our model features three key components: an ambient underwater optical constant, a phase function for angular scattering, and a pixel position modulation function for spatial variations in backscatter. Based on this model, we propose a restoration algorithm that leverages complementary information across subaperture views. Experimental results demonstrate that our approach significantly outperforms state-of-the-art underwater image enhancement techniques across multiple metrics and diverse underwater scenes, establishing a promising new direction for underwater imaging applications.

Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm per-formance significantly. However, existing methods attempt to extract domain-invariant features, neglecting that the bi-ased data leads the network to learn biased features that are non-causal and poorly generalizable. To this end, we pro-pose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically, we formulate SDG in object detection from a causal perspective and construct a Struc-tural Causal Model (SCM) to analyze the data bias andfeature bias in the task, which are caused by scene confounders and object attribute confounders. Based on the SCM, we de-sign a Global-Local Transformation module for data aug-mentation, which effectively simulates domain diversity and mitigates the data bias. Additionally, we introduce a Causal Attention Learning module that incorporates a designed at-tention invariance loss to learn image-level features that are robust to scene confounders. Moreover, we develop a Causal Prototype Learning module with an explicit instance constraint and an implicit prototype constraint, which fur-ther alleviates the negative impact of object attribute con-founders. Experimental results on five scenes demonstrate the prominent generalization ability of our method, with an improvement of 3.9% mAP on the Night-Clear scene.

There exist multiple types of flyers in the world that either achieve lift through leveraging aerodynamics by cleverly choosing the wing airfoils of an airplane, or by flapping their wings. As the flapping wing vehicles (FWVs) decrease in scale, lift is predominantly generated by flapping. However, bigger birds or bird-scale flapping wing vehicle (BFWV) may not be able to hover but leverage the forward velocity to augment the lift generation. In this paper, we analyze the aerodynamic lift augmentation through the translational velocity for a 12g, BFWV with tail (flapping around 14 Hz). We prove that the vehicle is unable to hover, but there exist a significant augmentation through the translational motion. A cycle-averaged aerodynamic model including the contribution from the translational velocity is proposed, and experimentally validated. Finally, the analysis of the sufficient conditions on the velocity direction and orientation are studied to maintain the zero angular moment forward flight.

In the field of image descattering, the image formation models employed for restoration approaches are often simplified. In these models, scattering distribution is uniform in homogeneous media when transmission is fixed. Through specifically designed experiments, we discover that scattering exhibits non-uniform characteristics even in homogeneous media. Neglecting non-uniform scattering in these models limits their accuracy in representing scattering distribution, resulting in existing image descattering approaches inadequate. To tackle these issues, this paper proposes a novel image formation model for image descattering, considering more physical parameters, such as zenith angle, azimuth angle, scattering phase function, and camera focal length. Our model describes the light transfer process in scattering media more accurately. For image descattering, we introduce corresponding algorithms for parameter estimation in our model and simultaneous restoration from degraded images. Experimental evaluations demonstrate the effectiveness of our proposed model in various tasks, including physical parameter estimation, pure-scattering removal, image dehazing, and underwater image restoration. In terms of calculating parameters, our results are close to the real values; in terms of underwater image restoration, our work outperforms the state-of-art methods; in terms of image dehazing, our work promotes the performance of existing methods by replacing previous models with our model.

The standard local binary pattern (LBP) operator shows its versatility in performing image classification-related tasks, including texture analysis, object recognition, and steganalysis. However, a conventional well-designed scheme utilizing LBP operator and histogram-based features does not have obvious advantage when compared with the well-known steganalytic scheme spatial rich model (SRM). In this paper, we propose an adapted LBP version, called threshold LBP (TLBP), to reveal the artifacts caused by data embedding. In the proposed steganalytic scheme, the TLBP operation is performed on residual images which are obtained by using a set of high-order derivative filters to capture intricate relationships among pixels. After performing TLBP operation, second order co-occurrence matrix features are formed and then processed with aggregation and non-linear mapping for boosting feature effectiveness. Experimental results show that the proposed TLBP features prevail over SRM features under various steganographic conditions.