Two-dimensional satellite image compression using compressive sensing

Compressive sensing is receiving a lot of attention from the image processing research community as a promising technique for image recovery from very few samples. The modality of compressive sensing technique is very useful in the applications where it is not feasible to acquire many samples. It is also prominently useful in satellite imaging applications since it drastically reduces the number of input samples thereby reducing the storage and communication bandwidth required to store and transmit the data into the ground station. In this paper, an interior point-based method is used to recover the entire satellite image from compressive sensing samples. The compression results obtained are compared with the compression results from conventional satellite image compression algorithms. The results demonstrate the increase in reconstruction accuracy as well as higher compression rate in case of compressive sensing-based compression technique.

Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning

In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio.

A single latent channel is sufficient for biomedical image segmentation

Glottis segmentation is a crucial step to quantify endoscopic footage in laryngeal high-speed videoendoscopy. Recent advances in using deep neural networks for glottis segmentation allow a fully automatic workflow. However, exact knowledge of integral parts of these segmentation deep neural networks remains unknown. Here, we show using systematic ablations that a single latent channel as bottleneck layer is sufficient for glottal area segmentation. We further show that the latent space is an abstraction of the glottal area segmentation relying on three spatially defined pixel subtypes. We provide evidence that the latent space is highly correlated with the glottal area waveform, can be encoded with four bits, and decoded using lean decoders while maintaining a high reconstruction accuracy. Our findings suggest that glottis segmentation is a task that can be highly optimized to gain very efficient and clinical applicable deep neural networks. In future, we believe that online deep learning-assisted monitoring is a game changer in laryngeal examinations.

A Legorization method based on 3D color printing trajectory

In this paper, a Legorization method which can reconstruct LEGO model with complex internal and external structures from 3D color printing trajectory is proposed. Different from voxelization methods, by combining advanced adaptive slicing algorithm with building “high-resolution” regions with thin plates, the reconstruction accuracy of initial LEGO units can be guaranteed. Furthermore, the tree structure is employed for automatically generating support structures which can be converted into LEGO support structures. By adopting split assembly appropriately and implementing combination of these parts accurately, the reducing supporting structures can be further simplified. In order to optimize the Legorization scheme, a machine learning method is used to guarantee the quality and efficiency of the reconstruction work. Complex LEGO models are provided to demonstrate the effectiveness of the proposed method.

HiC-GNN: A Generalizable Model for 3D Chromosome Reconstruction Using Graph Convolutional Neural Networks

Chromosome conformation capture (3C) is a method of measuring chromosome topology in terms of loci interaction. The Hi-C method is a derivative of 3C that allows for genome wide quantification of chromosome interaction. From such interaction data, it is possible to infer the three-dimensional (3D) structure of the underlying chromosome. In this paper, we use a node embedding algorithm and a graph neural network to predict the 3D coordinates of each genomic loci from the corresponding Hi-C contact data. Unlike other chromosome structure prediction methods, our method can generalize a single model across Hi-C resolutions, multiple restriction enzymes, and multiple cell populations while maintaining reconstruction accuracy. We derive these results using three separate Hi-C data sets from the GM12878, GM06990, and K562 cell lines. We also compare the reconstruction accuracy of our method to four other existing methods and show that our method yields superior performance. Our algorithm outperforms the state-of-the-art methods in the accuracy of prediction and introduces a novel method for 3D structure prediction from Hi-C data.

Effects of Different Parameter Settings for 3D Data Smoothing and Mesh Simplification on Near Real-Time 3D Reconstruction of High Resolution Bioceramic Bone Void Filling Medical Images

Three-dimensional reconstruction plays a vital role in assisting doctors and surgeons in diagnosing the healing progress of bone defects. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, this study omits the volume rendering methods. Many improvements have been made to surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images and studying the effects of different parameter settings for the improvements. Hence, this study attempts near real-time surface rendering for large medical images. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 5 and mesh simplification reduction factor of 0.1 is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61%, respectively.

The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images

Interferometric multispectral images contain rich information, so they are widely used in aviation, military, and environmental monitoring. However, the abundant information also leads to the disadvantages that longer time and more physical resources are needed in signal compression and reconstruction. In order to make up for the shortcomings of traditional compression and reconstruction algorithms, the stacked convolution denoising autoencoder (SCDA) reconstruction algorithm for interference multispectral images is proposed in this paper. And, the experimental code based on the TensorFlow system is built to reconstruct these images. The results show that, compared with D-AMP and ReconNet algorithms, the SCDA algorithm has the advantages of higher reconstruction accuracy and lower time complexity and space complexity. Therefore, the SCDA algorithm proposed in this paper can be applied to interference multispectral images.

The Reconstruction Accuracy of the Hip Center of Rotation After Cementless Total Hip Arthroplasty for Failed Treatment of Acetabular Fractures. Does It Matter?

Background To assess the impaction of reconstruction accuracy of hip center of rotation (COR) on midterm clinical and radiographic results of cementless reconstruction of total hip arthroplasties (THAs) for patients after failed treatment of acetabular fractures. Methods One hundred and four patients (107 hips) who underwent THAs after failed treatment of acetabular fractures were retrospectively evaluated and cementless cups and stems were implanted in all hips. Clinical outcomes were assessed using the Harris hip score (HHS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) scoring system. Radiographic results were analyzed by serial perioperative x-rays. Results At the latest follow-up examination, the median HHS increased from 52 (42-65) before surgery to 93 (90-97) (p < 0.001) and the median WOMAC decreased from 52 (36-65) before surgery to 5.8 (1.5-8) (p < 0.001). Compared with normal contralateral hip, 79 cups migrated superiorly (0.2-33.6mm) and 22 cups migrated inferiorly (0.2-16.1mm). The distance of superior migration of reconstructed COR was correlated with positive Trendelenburg sign at the latest follow-up examination (r=0.504; p < 0.001). The percentage of postoperative Trendelenburg sign was significantly higher in superior migration subgroup than that in subgroup with anatomical restoration of COR (P=0.015). Conclusions Cementless THAs in patients after failed treatment for acetabular fractures achieved predictable clinical and radiographic outcomes. A superiorly migrated hip COR appeared to exert a negative effect on abductor muscle function.