Point cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recent years. This paper provides a comprehensive survey of point cloud upsampling algorithms. We classify existing point cloud upsampling algorithms into optimization-based methods and deep learning-based methods, and analyze the advantages and limitations of different algorithms from a modular perspective. In addition, we cover some other important issues such as public datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future research directions and open issues that should be further addressed.

Protein crystallization is the bottleneck in macromolecular crystallography, and crystal recognition is a very important step in the experiment. To improve the recognition accuracy by image classification algorithms further, the Mask R-CNN model is introduced for the detection of protein crystals in this paper. Because the protein crystal image is greatly affected by backlight and precipitate, the contrast limit adaptive histogram equalization (CLAHE) is applied with Mask R-CNN. Meanwhile, the Transfer Learning method is used to optimize the parameters in Mask R-CNN. Through the comparison experiments between this combined algorithm and the original algorithm, it shows that the improved algorithm can effectively improve the accuracy of segmentation.

Aiming at the problem of insufficient feature extraction, low precision, and recall in sea surface ship detection, a YOLOv5 algorithm based on lightweight convolution and attention mechanism is proposed. We combine the receptive field enhancement module (REF) with the spatial pyramid rapid pooling module to retain richer semantic information and expand the sensory field. The slim-neck module based on a lightweight convolution (GSConv) is added to the neck section, to achieve greater computational cost-effectiveness of the detector. And, to lift the model’s performance and focus on positional information, we added the coordinate attention mechanism. Finally, the loss function CIoU is replaced by SIoU. Experimental results using the seaShips dataset show that compared with the original YOLOv5 algorithm, the improved YOLOv5 algorithm has certain improvements in model evaluation indexes, while the number of parameters in the model does not increase significantly, and the detection speed also meets the requirements of sea surface ship detection.

Aiming at the problem of single stitching scenes (ordered or disordered images), which are time-consuming and involve complex computation during the image stitching, a multi-scene image stitching method based on merge sorting is proposed called merge-sorting image stitching (MSIS). We established an image stitching model based on merge sorting, directly stitching the matched images without traversing all the images and sorting them. Compared with the traditional method, this method significantly reduces the computational time taken matching irrelevant image pairs and improves the efficiency of image registration and stitching. The experimental results show that, compared with the binary tree-based model, our method reduces the time required to complete image stitching by about 10% and the distortion of the stitched images.

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations.