In this work, we irradiated a high-definition (HD) industrial camera based on a commercial-off-the-shelf (COTS) CMOS image sensor (CIS) with Cobalt-60 gamma-rays. All components of the camera under test were fabricated without radiation hardening, except for the lens. The irradiation experiments of the HD camera under biased conditions were carried out at 1.0, 10.0, 20.0, 50.0 and 100.0 Gy/h. During the experiment, we found that the tested camera showed a remarkable degradation after irradiation and differed in the dose rates. With the increase of dose rate, the same target images become brighter. Under the same dose rate, the radiation effect in bright area is lower than that in dark area. Under different dose rates, the higher the dose rate is, the worse the radiation effect will be in both bright and dark areas. And the standard deviations of bright and dark areas become greater. Furthermore, through the progressive degradation analysis of the captured image, experimental results demonstrate that the attenuation of signal to noise ratio (SNR) versus radiation time is not obvious at the same dose rate, and the degradation is more and more serious with increasing dose rate. Additionally, the decrease rate of SNR at 20.0, 50.0 and 100.0 Gy/h is far greater than that at 1.0 and 10.0 Gy/h. Even so, we confirm that the HD industrial camera is still working at 10.0 Gy/h during the 8 h of measurements, with a moderate decrease of the SNR (5 dB). The work is valuable and can provide suggestion for camera users in the radiation field.

In recent years, with the rapid development of unmanned aerial vehicle (UAV) technology, multi-view 3D reconstruction has once again become a hot spot in computer vision. Incremental Structure From Motion (SFM) is currently the most prevalent reconstruction pipeline, but it still faces challenges in reconstruction efficiency, accuracy, and feature matching. In this paper, we use deep learning algorithms for feature matching to obtain more accurate matching point pairs. Moreover, we adopted the improved Gauss–Newton (GN) method, which not only avoids numerical divergence but also accelerates the speed of bundle adjustment (BA). Then, the sparse point cloud reconstructed by SFM and the original image are used as the input of the depth estimation network to predict the depth map of each image. Finally, the depth map is fused to complete the reconstruction of dense point clouds. After experimental verification, the reconstructed dense point clouds have rich details and clear textures, and the integrity, overall accuracy, and reconstruction efficiency of the point clouds have been improved.

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.

This work introduces a two-stage framework, named the Distorted underwater image Restoration Network (DR-Net), to address the complex degradation of underwater images caused by turbulence, water flow fluctuations, and optical properties of water. The first stage employs the Distortion Estimation Network (DE-Net), which leverages a fusion of Transformer and U-Net architectures to estimate distortion information from degraded images and focuses on image distortion recovery. Subsequently, the Image Restoration Generative Adversarial Network (IR-GAN) in the second stage utilizes this estimated distortion information to deblur images and regenerate lost details. Qualitative and quantitative evaluations on both synthetic and real-world image datasets demonstrate that DR-Net outperforms traditional methods and restoration strategies from different perspectives, showcasing its broader applicability and robustness. Our approach enables the restoration of underwater images from a single frame, which facilitates the acquisition of marine resources and enhances seabed exploration capabilities.

To solve the problems of color distortion and blur in underwater images, the paper compensates the loss of the red channel after color correcting process, and uses the image information itself as the basis to estimate the fuzzy kernel to restore the final image. Firstly, stretch image gray value range to correct color cast and improve contrast. The redundant noise in red channel of the image is removed by median filtering to reduce interference. Subsequently, the pre-processed red channel and green channel of the image are split into high frequency and low frequency components by using SYM4 as the wavelet basis, and then the fusion is performed respectively. The new red channel after information compensation is obtained by reconstruction based on inverse wavelet transform. Then, the algorithm of the reweighted graph total variation prior combined with multi-resolution image pyramiding strategy is used to obtain the fuzzy kernel estimation. Finally, the image is restored by using a non-blind recovery algorithm based on ring suppression, which effectively suppresses the ringing effect while adequately preserving details. The simulation results of the proposed algorithm and several classical algorithms show that the proposed algorithm can effectively remove blur and improve image clarity while enhancing image and correcting color deviation.

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.