Blind2Sound: Self-Supervised Image Denoising without Residual Noise

Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images

Landau-Zener-Stuckelberg-Majorana interference in a 3D transmon driven by a chirped microwave

By driving a 3D transmon with microwave fields, we generate an effective avoided energy-level crossing. Then we chirp microwave frequency, which is equivalent to driving the system through the avoided energy-level crossing by sweeping the avoided crossing. A double-passage chirp produces Landau-Zener-St\"uckelberg-Majorana interference that agree well with the numerical results. Our method is fully applicable to other quantum systems that contain no intrinsic avoided level crossing, providing an alternative approach for quantum control and quantum simulation

Research on extraction method of ground fissures caused by mining through UAV image in coal mine areas

In order to promptly and exactly identify the mining ground fissures in coal mining areas, and avoid the secondary geological disasters, as well as restore the land ecological environment in the coal mining areas, this study focused on the extraction method of surface mining induced fissures, with the fissure development zone of coal mining face of Ningtiaota Coal Mine as the study area, which was located in the northwest of Shenmu County, Yulin City, Shaanxi Province. Meanwhile, the smooth execution of this research was based on low-altitude UAV remote sensing images, field surveys, and the construction of an object-oriented supervision classified model method. The images acquisition process was shown as follows: Firstly, the candidate segmentation parameters were obtained utilizing the ESP（Estimation of scale parameter）optimal segmentation scale evaluation tool, and then the optimal segmentation parameters were determined immediately combining visual interpretation, finally the image objects such as fissures and vegetation were obtained. 15 optimized feature parameters were determined from 24 initial feature sets to construct the optimized feature set with the feature space optimization tool. On this basis, a variety of machine learning classifier models were combined, such as Support Vector Machine, K Nearest Neighbor, Random Forest, Naive Bayes, etc. The experimental analysis results presented that the classification effect and accuracy of the land features were consistent. The SVM classification method had the best overall effect, performing best in the four erroneously partitioned domains, with the least number of misclassified small patches. The overall classification accuracy achieved 88.97%, and the Kappa coefficient attained 0.849. In addition, the F1 value of crack extraction accuracy reached 87.87%, with the Kappa coefficient amount to 0.848. The overall classification accuracy of the four classification methods was above 80%. The optimal model method accurately extracted 10 main fissures in the research area, which was more efficient than traditional manual vectorization. The surface mining fissures could be effectively extracted by the aid of low-altitude drone remote sensing images and object-oriented methods. This research could provide technical support for the investigation and monitoring of geological disasters caused by coal mining subsidence and land ecological restoration

Absorption spectra of superconducting qubits driven by bichromatic microwave fields

We report experimental observation of two distinct quantum interference patterns in the absorption spectra when a transmon superconducting qubit is subjected to a bichromatic microwave field with the same Rabi frequencies. Within the two-mode Floquet formalism with no dissipation processes, we propose a graph-theoretical representation to model the interaction Hamiltonian for each of these observations. This theoretical framework provides a clear visual representation of various underlying physical processes in a systematic way beyond rotating-wave approximation. The presented approach is valuable to gain insights into the behavior of multichromatic field driven quantum two-level systems, such as two-level atoms and superconducting qubits. Each of the observed interference patterns is represented by appropriate graph products on the proposed color-weighted graphs. The underlying mechanisms and the characteristic features of the observed fine structures are identified by the transitions between the graph vertices, which represent the doubly dressed states of the system. The good agreement between the numerical simulation and experimental data confirms the validity of the theoretical method. Such multiphoton interference may be used in manipulating the quantum states and/or generate nonclassical microwave photons

Forecasts of CMB lensing reconstruction of AliCPT-1 from the foreground cleaned polarization data

Cosmic microwave background radiation (CMB) observations are unavoidably contaminated by emission from various extra-galactic foregrounds, which must be removed to obtain reliable measurements of the cosmological signal. In this paper, we demonstrate CMB lensing reconstruction in AliCPT-1 after foreground removal, combine the two bands of AliCPT-1 (90 and 150~GHz) with Planck HFI bands (100, 143, 217 and 353~GHz) and with the WMAP-K band (23~GHz). In order to balance contamination by instrumental noise and foreground residual bias, we adopt the Needlet Internal Linear Combination (NILC) method to clean the E-map and the constrained Internal Linear Combination (cILC) method to clean the B-map. The latter utilizes additional constraints on average frequency scaling of the dust and synchrotron to remove foregrounds at the expense of somewhat noisier maps. Assuming 4 modules observing 1 season from simulation data, the resulting effective residual noise in E- and B-map are roughly $15~\mu{\rm K}\cdot{\rm arcmin}$ and $25~\mu{\rm K}\cdot{\rm arcmin}$, respectively. As a result, the CMB lensing reconstruction signal-to-noise ratio (SNR) from polarization data is about SNR$\,\approx\,$4.5. This lensing reconstruction capability is comparable to that of other stage-III small aperture millimeter CMB telescopes.Comment: 12 pages, 6 figure

Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence

Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research applications. Diffusion MRI (DWI) acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than single-shot, but suffers from inter-shot motion-induced artifacts. These artifacts cannot be removed prospectively, leading to the absence of artifact-free training labels. Thus, the potential of deep learning in multi-shot DWI reconstruction remains largely untapped. To break the training data bottleneck, here, we propose a Physics-Informed Deep DWI reconstruction method (PIDD) to synthesize high-quality paired training data by leveraging the physical diffusion model (magnitude synthesis) and inter-shot motion-induced phase model (motion phase synthesis). The network is trained only once with 100,000 synthetic samples, achieving encouraging results on multiple realistic in vivo data reconstructions. Advantages over conventional methods include: (a) Better motion artifact suppression and reconstruction stability; (b) Outstanding generalization to multi-scenario reconstructions, including multi-resolution, multi-b-value, multi-undersampling, multi-vendor, and multi-center; (c) Excellent clinical adaptability to patients with verifications by seven experienced doctors (p<0.001). In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.Comment: 23 pages, 16 figure

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl

Response of Land Use and Net Primary Productivity to Coal Mining: A Case Study of Huainan City and Its Mining Areas

The terrestrial ecosystem carbon cycle is essential to the global carbon cycle. Mining activities have seriously damaged the terrestrial ecosystem and destroyed the carbon sequestration ability of vegetation, which is of great significance to studying the effect of coal mining on land structure change and carbon sink function in cities and mining areas. However, the existing research lacks the targeted analysis of the carbon sink level of the mining area combined with the mining data. Based on the coal-mining information, land-use data, and MODIS NPP data, this study analyzed the spatio-temporal change characteristics of land use and NPP in Huainan City and its mining areas from 2001 to 2020. The results showed that: (1) 22.5% of the land types in the mining area have changed, much higher than 3.2% in Huainan; 40.08 km2 of the cropland in the mining area has been transformed into waterbodies, seriously affecting regional food security. (2) NPP fluctuates with rainfall, has a weak correlation with temperature, and is restricted by coal-mining factors. The average NPP of most coal mines is significantly lower than that of non-mining areas. The NPP of Huainan City showed an overall growth trend of 2.20 g/(m2 × a), which was much higher than the average value of 0.43 g/(m2 × a) in the mining area. Especially in the Guqiao mine, the difference in NPPslope before and after mining was as high as 16.92 g/(m2 × a). (3) The probability integral method was used to estimate that 195.16 km2 of land in Huainan would be damaged by mining in 2020. The distribution of damage degree was negatively correlated with NPPslope, which meant the more serious the damage was, the less NPPslope was. This study revealed the characteristics of land-use change and NPP spatio-temporal response in resource-based cities and mining-disturbed areas. It quantitatively estimated the impact of mining activities on regional carbon sink function. It can provide theory and data support for mining areas to carry out ecological protection and restoration, improve the environmental service function of resource-based cities, and formulate sustainable development strategies

Assessment of Spatial Temporal Changes of Ecological Environment Quality: A Case Study in Huaibei City, China

Under the short-term economic development goal, the excessive exploitation of natural resources and the destruction of the ecological environment make the ecological environment of Huaibei cities increasingly fragile. This study constructed the Remote Sensing Ecological Index (RSEI) to evaluate the ecological environment change trend and its driving factors in Huaibei City from 2000 to 2020. The barycenter migration model was used to determine the RSEI spatial change trend, and the geographic detector was used to analyze the influencing factors of the RSEI value change. The results showed that: (1) the average RSEI value of Huaibei City generally fluctuates within the range of good and excellent grades. (2) The migration direction of the barycenter of RSEI is similar when the level of RSEI improves or decreases from 2000 to 2020, and the barycenter migration is most severe from 2005 to 2015. (3) The driving factors of RSEI change were population density (0.47) > land use (0.24) > slope (0.14) > precipitation (0.08) > temperature (0.04) > altitude (0.03). All the factors had interaction effects on the RSEI, mainly with nonlinear enhancement. (4) From 2000 to 2010, urban construction encroached on all kinds of land, which was the direct reason for the decline in ecological environment quality. From 2010 to 2020, the surge of water and meadow areas improved the ecological environment quality of Huaibei city. Therefore, reducing the expansion of artificial land, returning farmland to forests and meadows, wetland park construction, and other ecological protection measures are the keys to ensuring the sustainable development of regional social and economic development. This study can provide a reference and scientific basis for sustainable development strategy and ecological protection planning to improve the ecological environment quality of Huaibei City

Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots

Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile, supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised denoisers, which learn only from single noisy images, solve the data collection problem. However, self-supervised denoising methods, especially blindspot-driven ones, suffer sizable information loss during input or network design. The absence of valuable information dramatically reduces the upper bound of denoising performance. In this paper, we propose a simple yet efficient approach called Blind2Unblind to overcome the information loss in blindspot-driven denoising methods. First, we introduce a global-aware mask mapper that enables global perception and accelerates training. The mask mapper samples all pixels at blind spots on denoised volumes and maps them to the same channel, allowing the loss function to optimize all blind spots at once. Second, we propose a re-visible loss to train the denoising network and make blind spots visible. The denoiser can learn directly from raw noise images without losing information or being trapped in identity mapping. We also theoretically analyze the convergence of the re-visible loss. Extensive experiments on synthetic and real-world datasets demonstrate the superior performance of our approach compared to previous work. Code is available at https://github.com/demonsjin/Blind2Unblind.Comment: Accepted to CVPR202