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With the rapid development of light detection and ranging (LiDAR) technology, multispectral LiDAR (MSL) can realize three-dimensional (3D) imaging of the ground object by acquiring rich spectral information. Although color restoration has been achieved on the basis of the full-waveform data of MSL, further improvement of the visual effect of color...
Citations
... Next, the saliency weight (SYW) is determined for both images A and G to highlight the salient items that their eminence is attenuated when captured in an underwater environment. This is done using a frequency-tuned (FT) algorithm for salient area recognition proposed by [27]. Both G and A images must be processed by the FT algorithm to produce two saliency weights that are needed later when computing the normalized weights required for the fusion process. ...
Humanity currently lives in a technological era that witnesses rapid progress in multiple fields. Digital image processing is one of the modern technologies that has provided practical answers to many challenges including image enhancement, analysis, reconstruction, recovery, compression, processing, and understanding. One of these notable challenges relates to underwater photography. Underwater images are always exposed to less-than-ideal conditions due to environmental and physical factors. These include refraction of light in water, scattering of particles and dust in the aquatic medium, lack of illumination in deep water, and poor contrast. These challenges make it extremely difficult to analyze and extract valuable information without advanced processing. In this study, an improved color balance-fusion algorithm is provided by improving the image visuality and modifying some equations to obtain sharper and clearer images. The proposed algorithm begins by finding the white balance of the input RGB color image, after that, it improves the intensity. Next, the edges are improved using Gamma separately. The weights are then found for each image and combined to find naive fusion. The resulting image is processed using a color retrieval algorithm to produce the final image. along with comparisons to eleven other algorithms with various processing methods. Experimental results showed that this algorithm can significantly improve underwater images, increasing image clarity and making colors clearer. The improvement rates reached 5.8389 and 2.6778 for UISM and UICM metrics, respectively.
In the acquisition process of 3D cultural relics, it is common to encounter noise. To facilitate the generation of high-quality 3D models, we propose an approach based on graph signal processing that combines color and geometric features to denoise the point cloud. We divide the 3D point cloud into patches based on self-similarity theory and create an appropriate underlying graph with a Markov property. The features of the vertices in the graph are represented using 3D coordinates, normal vectors, and color. We formulate the point cloud denoising problem as a maximum a posteriori (MAP) estimation problem and use a graph Laplacian regularization (GLR) prior to identifying the most probable noise-free point cloud. In the denoising process, we moderately simplify the 3D point to reduce the running time of the denoising algorithm. The experimental results demonstrate that our proposed approach outperforms five competing methods in both subjective and objective assessments. It requires fewer iterations and exhibits strong robustness, effectively removing noise from the surface of cultural relic point clouds while preserving fine-scale 3D features such as texture and ornamentation. This results in more realistic 3D representations of cultural relics.
The information extracted from waveform data of full-waveform light detection and ranging (LiDAR) has been widely used in applications such as 3D urban modeling, target recognition, and classification However, the presence of weak signals is inevitable in LiDAR systems. To enhance its effective detection capability and extraction accuracy, we propose a multispectral LiDAR (MSL) weak signals extraction (MSL-WSE) method. The measurement data from our MSL system were used to evaluate the performance of the proposed method. The correlation coefficient (R
2
), root mean square error (RMSE) and effective extraction rate show that the MSL-WSE method accurately detected and extracted the waveform parameters of weak echo signals, providing the more realistic and fine-grained true color 3D point cloud.
