Deep-sea exploration has garnered increasing global interest. Non-uniform illumination and scattering from suspended particles in deep-sea environments significantly impair underwater image visibility. However, existing methods have limitations when dealing with strongly scattered scenes under non-uniform lighting conditions. To address these challenges, we introduce a novel deep-sea scattering model specifically tailored to the unique optical properties of the deep-sea environment, with a particular focus on the non-uniform active lighting conditions. We then propose a reconstruction algorithm that restores images by solving an optimization problem, integrating both the physical scattering model and a visual correction term. Evaluations across six deep-sea scenarios demonstrate superior performance, achieving 40% lower FADE scores and 5% higher HyperIQA scores than state-of-the-art methods, with notable improvements in texture preservation and color fidelity.

Light field (LF) atmospheric descattering methods using multi-view images from camera arrays offer significant advantages for solving strong scattering due to their ability in exploiting high-dimensional light information. However, the relationship between performance and scattered LF sampling rate (i.e., the density of samples per unit area) is an unknown coupling, affecting acquisition and processing complexity. In this paper, we define the minimum atmospheric scattered LF sampling rate under optimal descattering quality, based on attenuated spectral support in scattering scenarios derived from the proposed atmospheric point spread function (APSF). The proposed APSF integrates the camera model, radiative transfer equation, and modified generalized Gaussian distribution (GGD) to describe multiple scattering. For any scattering parameters, the proposed APSF can be directly derived without infinite series, ensuring full adaptability to all acquisition systems through the integration of system model. Combining APSF with scene and acquisition system information, the scattered LF spectrum is determined, and consequently the minimum atmospheric scattered LF sampling rate is derived for the first time. Experimental results demonstrate the accuracy, effectiveness, and robustness of the proposed atmospheric scattered LF sampling theory through comparisons of atmospheric descattering performance across different LF sampling rates, object types, scene depths, and scattering intensities. The proposed method achieves a reduction in the number of acquisition cameras by an average of 78.4% while maintaining processing quality, which significantly enhances the applicability of LF atmospheric descattering methods.

High-quality annotated data is crucial in semantic segmentation. However, existing datasets either provide single view images or offer small baseline multi-angle light field images with annotations only for the central image, hindering the development of multi-angle perception capabilities. In this paper, we introduce the first large-baseline light field multimodal semantic segmentation dataset collected using a 3×3 camera array and a lidar. To the best of our knowledge, it is the largest light field multimodal dataset with 623 fully annotated light field images of each view and 623 frame point clouds. Then, we propose a baseline method PSPNet_LGA for all light field image segmentation, which synergizes local information from each angle with the global features of the light field, facilitating segmentation of each perspective. Our experimental results reveal that our method achieves an average improvement of 1.13 mIoU compared to the single-image semantic segmentation baseline. Additionally, we offer a real-scene dataset with highly accurate ground truth for light field depth estimation, establishing a benchmark in this area. Our dataset and code will be made available at https://github.com/rogercomeon/TrafficScene.

Windowed six degrees of Freedom (Windowed-6DoF) virtual reality (VR) content that provides users an immersive feeling of walking through a 3D 360 VR space with constrained rotational movements around X and Y axes and constrained translational movements along Z axis is important for the development of VR. To facilitate this windowed-6DoF immersive feeling, light fields (LFs) from multiple perspectives within the windowed 6-DoF space are captured. In contrast to employing a large-scale camera array for LF capture, utilizing hand-held plenoptic cameras offers a more portable and versatile solution, thereby promoting practical applications. However, how to stitch the LFs at different rotational angles containing motion parallax is challenging. In this paper, a novel LF stitching method is proposed to generate windowed-6DoF LFs. First, multi-concentric spherical modeling is proposed to parameterize the recorded LFs to eliminate projection biases in the registration process. Then, a global-local adaptive LF registration is proposed by developing incremental multi-layer global-local adaptive homographies based on the 4D light field feature (LiFF), incremental strategy and depth layer maps (DLMs) to eliminate parallax errors. Testing on the LFs captured in both indoor and outdoor scenes with different focal lengths, quantities of LFs and scales of translation and rotation, the proposed method outperforms the existing approaches in terms of subjective quality, objective quality, light field consistency and content production robustness, which can produce VR content of superior quality more reliably.

Stereo vision is one of the most important ways to explore the underwater world. However, the refraction occurs on the dome port camera housing, resulting in distorted images and errors in the computed 3D information. In this paper, we proposed an underwater refractive stereo vision method that can calibrate the camera’s parameters and obtain 3D information underwater. First, a dome port refractive ray tracing model is proposed to describe the projection relation between the object point and the image point. Then, a refractive calibration method is proposed to calibrate the camera’s intrinsic and extrinsic parameters without pre-calibration in air. Finally, a refractive rectification method is proposed for disparity search along polar lines and calculating 3D information. Experimental results show that the proposed method outperforms existing methods in both simulation and real imaging scenarios.

The image sequences captured by Unmanned Aerial Vehicles (UAVs) can be applied to many computer vision tasks. However, due to the instability of UAV flight, the captured image sequences will deviate from the preset trajectory and pose, which reduce the quality of subsequent applications such as panoramic image stitching. In this paper, a novel method is proposed to rectify UAV-captured image sequences by transforming the images to a regular trajectory with the uniform pose. First, to minimize the total transformation deviation, virtual regular camera trajectory is derived by minimizing the global error of coordinates between actual and virtual camera trajectories. Then, camera-pose-relevant local homography is proposed by inserting the camera pose into local homography to transform the images to the derived virtual trajectory with the uniform pose and correct translation parallax. The experimental results demonstrate the effectiveness of the proposed rectification algorithm from both theoretical and application levels.