The non-invasive measurement of hemoglobin oxygen saturation (SO₂) in retinal vessels is based on spectrophotometry and the absorption spectral characteristics of the tissue. The dual-wavelength retinal images are simultaneously captured via retinal oximetry. SO₂ is calculated by processing a series of images and by calculating the optic density ratio of two images. However, existing SO₂ research is focused on the thick vessels in the high-clarity region of retinal images. However, the thin vessels in the low-clarity region could provide significant information for the detection and diagnosis of neovascular diseases. To this end, we proposed a novel hybrid vessel segmentation algorithm. Firstly, a median filter was employed for image denoising. Secondly, high- and low-clarity region segmentation was carried out based on a clarity histogram. The vessels in the high-clarity areas were segmented after implementing a Gaussian filter, a matched filter, and morphological segmentation. Additionally, the vessels in the low-clarity areas were segmented using a guided filter, matched filtering, and dynamic threshold segmentation. Finally, the results were obtained through image merger and morphological operations. The experimental results and analysis show that the proposed method can effectively segment thick and thin vessels and can extend the measuring range of dual-wavelength retinal oximetry.

The ${\mathrm{SO}}_2$ in retinal vessels can provide doctors with important clinical information about the metabolic state of the retina.¹ For example, assessment of ${\mathrm{SO}}_2$ in the optic nerve head may help with early diagnosis of glaucoma and is crucial for timely effective treatment.² Monitoring ${\mathrm{SO}}_2$ changes in the retina could evaluate relationships between oxygen consumption, blood sugar levels, and vascular function in diabetic retinopathy.³ Therefore, it is important to obtain retinal oxygen saturation, which could be used to monitor retinal disorders and improve the efficiency and accuracy of diagnosis.

Retinal images at 570nm and 600nm are simultaneously captured by retinal oximetry. However, there exists misalignment between the two images, which should be adjusted before they are used to analyze hemoglobin oxygen saturation (SO2). Having a robust, accurate and fast registration scheme can ensure accuracy and speed of SO2 calculation. However, the existing retinal image registration methods cannot meet demand for clinical application of retinal oximetry. For example, errors in the vessel segmentation from each image cause corresponding errors in the registration, point matches may become unreliable when the predefined matching cost is corrupted by pathological retinal images. And it is always a time-consuming task. In this paper, we first analyzed the misalignment reason between dual-wavelength images, and concluded that there only exists the rotation and translation deviation between the two images caused by a mounting deviation of CCD cameras. The parameters were only related to system itself. Based on this, we proposed a registration method based on camera calibration that benefits from feature extraction and affine transformation matrix calculation. The parameters obtained by the proposed method were tested on 80 pairs of retinal images of 5 healthy volunteers and the results showed that the proposed method can successfully register the dual-wavelength images. The proposed registration method can significantly improve SO2 calculation speed while ensuring accuracy.

Panoramic imaging and state recognition technology for borehole environments in rock strata, as an intuitive method for investigating the stratigraphic distribution of target regions, offers advantages such as intuitive detection results, high accuracy in stratum state identification, and reliable detection technology. Research on this technology holds significant academic value and engineering importance. In this study, based on a dual-camera module combining a fisheye lens and a high-resolution lens, a compact and lightweight borehole imaging system integrating wide-field-of-view and high-resolution imaging was developed through structural optimization research. Subsequently, by analyzing the characteristics of the borehole inner wall in a simulated rock stratum environment, panoramic unwrapping of wide-field-of-view imaging results was achieved through image registration and coordinate mapping. Furthermore, an improved MobileViT network was proposed, which incorporates an efficient multi-scale attention mechanism to enhance multi-scale feature extraction and a cosine classifier to improve inter-class discriminability, and was combined with sliding window prediction to realize intelligent recognition of seven types of rock borehole images. Experimental results demonstrate that the vision system can achieve close-range, high-resolution panoramic imaging of long boreholes while enabling intelligent state assessment of rock materials. The recognition accuracy on the test set reached 92.12%. This study confirms that the proposed system is capable of comprehensive intelligent evaluation of geotechnical borehole conditions, providing a reliable and efficient technical means for geological investigation.

Underwater three-dimensional reconstruction is inherently challenged by light scattering and wavelength-dependent absorption, causing color distortion, contrast degradation, and limited visibility in recovered geometries. Existing underwater three-dimensional (3D) Gaussian splatting methods rely solely on pixel-wise losses [L1/structural similarity index measure (SSIM)], which produce over-smoothed textures in scattering environments, while their direction-only medium modeling assumes spatially homogeneous water properties that contradict real-world depth-dependent turbidity. To address these limitations, we present an enhanced framework for underwater 3D reconstruction integrating progressive perceptual supervision with spatial coordinate encoding. A progressively weighted learned perceptual image patch similarity (LPIPS) loss recovers fine texture details without compromising geometric accuracy, while sinusoidal pixel-coordinate encoding enables self-supervised modeling of spatially varying turbidity without depth sensors. Experimental evaluation on the SeaThru-NeRF dataset demonstrates substantial perceptual quality improvements, with the progressive LPIPS alone achieving up to 33.9% LPIPS reduction on high-turbidity scenes, while the full framework maintains photometric accuracy (peak signal-to-noise ratio/SSIM). Cross-dataset evaluation on the out-of-distribution, deep-sea Submerged3D dataset further confirms that these perceptual gains generalize beyond a single benchmark. The method achieves interactive rendering performance (15 to 35 frames per second) and efficient training (∼8 to 13 min), providing a practical solution for real-time underwater 3D reconstruction in robotic applications.