Sparse single-pixel imaging via optimization in nonuniform sampling sparsity.

Reducing the imaging time while maintaining reconstruction accuracy remains challenging for single-pixel imaging. One cost-effective approach is nonuniform sparse sampling. The existing methods lack intuitive and intrinsic analysis in sparsity. The lack impedes our comprehension of the form's adjustable range and may potentially limit our ability to identify an optimal distribution form within a confined adjustable range, consequently impacting the method's overall performance. In this Letter, we report a sparse sampling method with a wide adjustable range and define a sparsity metric to guide the selection of sampling forms. Through a comprehensive analysis and discussion, we select a sampling form that yields satisfying accuracy. These works will make up for the existing methods' lack of sparsity analysis and help adjust methods to accommodate different situations and needs.

Deep nonlocal low-rank regularization for complex-domain pixel super-resolution.

Pixel super-resolution (PSR) has emerged as a promising technique to break the sampling limit for phase imaging systems. However, due to the inherent nonconvexity of phase retrieval problem and super-resolution process, PSR algorithms are sensitive to noise, leading to reconstruction quality inevitably deteriorating. Following the plug-and-play framework, we introduce the nonlocal low-rank (NLR) regularization for accurate and robust PSR, achieving a state-of-the-art performance. Inspired by the NLR prior, we further develop the complex-domain nonlocal low-rank network (CNLNet) regularization to perform nonlocal similarity matching and low-rank approximation in the deep feature domain rather than the spatial domain of conventional NLR. Through visual and quantitative comparisons, CNLNet-based reconstruction shows an average 1.4 dB PSNR improvement over conventional NLR, outperforming existing algorithms under various scenarios.

Self-supervised learning for single-pixel imaging via dual-domain constraints.

Deep-learning-augmented single-pixel imaging (SPI) provides an efficient solution for target compressive sensing. However, the conventional supervised strategy suffers from laborious training and poor generalization. In this Letter, we report a self-supervised learning method for SPI reconstruction. It introduces dual-domain constraints to integrate the SPI physics model into a neural network. Specifically, in addition to the traditional measurement constraint, an extra transformation constraint is employed to ensure target plane consistency. The transformation constraint uses the invariance of reversible transformation to impose an implicit prior, which avoids the non-uniqueness of measurement constraint. A series of experiments validate that the reported technique realizes self-supervised reconstruction in various complex scenes without any paired data, ground truth, or pre-trained prior. It can tackle the underdetermined degradation and noise, with ∼3.7-dB improvement on the PSNR index compared with the existing method.

Tuning-free and self-supervised image enhancement against ill exposure.

Complex lighting conditions and the limited dynamic range of imaging devices result in captured images with ill exposure and information loss. Existing image enhancement methods based on histogram equalization, Retinex-inspired decomposition model, and deep learning suffer from manual tuning or poor generalization. In this work, we report an image enhancement method against ill exposure with self-supervised learning, enabling tuning-free correction. First, a dual illumination estimation network is constructed to estimate the illumination for under- and over-exposed areas. Thus, we get the corresponding intermediate corrected images. Second, given the intermediate corrected images with different best-exposed areas, Mertens' multi-exposure fusion strategy is utilized to fuse the intermediate corrected images to acquire a well-exposed image. The correction-fusion manner allows adaptive dealing with various types of ill-exposed images. Finally, the self-supervised learning strategy is studied which learns global histogram adjustment for better generalization. Compared to training on paired datasets, we only need ill-exposed images. This is crucial in cases where paired data is inaccessible or less than perfect. Experiments show that our method can reveal more details with better visual perception than other state-of-the-art methods. Furthermore, the weighted average scores of image naturalness matrics NIQE and BRISQUE, and contrast matrics CEIQ and NSS on five real-world image datasets are boosted by 7%, 15%, 4%, and 2%, respectively, over the recent exposure correction method.

Robust phase unwrapping via non-local regularization.

Phase unwrapping is an indispensable step in recovering the true phase from a modulo-2π phase. Conventional phase unwrapping methods suffer from error propagation under severe noise. In this Letter, we propose an iterative framework for robust phase unwrapping with high fidelity. The proposed method utilizes the transport-of-intensity equation to solve the phase unwrapping problem with high computational efficiency. To further improve reconstruction accuracy, we take advantage of non-local structural similarity using low-rank regularization. Meanwhile, we use an adaptive iteration strategy that dynamically and automatically updates the denoising parameter to avoid over-smoothing and preserve image details. A set of simulation and experimental results validates the proposed method, which can provide satisfying results under severe noise conditions, and outperform existing state-of-the-art phase unwrapping methods with at least 6 dB higher peak SNR (PSNR).

2,3-Diaminophenazine@carbon felt with chemical grafting via amide bonds as an electrode in lithium-ion batteries.

Grafting organic molecules onto an insoluble matrix is an effective way to improve the electronic conductivity and insolubility in electrolyte of organic electrode materials. The active group of CN in DAP@C composites synthesized by chemical grafting of 2,3-diaminophenazine (DAP) with carbon felt through amide bonds (-CO-NH-) displays excellent electrochemical behavior.

Efficient large-scale single-pixel imaging.

The speed of single-pixel imaging (SPI) is tied to its resolution, which is positively related to the number of modulation times. Therefore, efficient large-scale SPI is a serious challenge that impedes its wide applications. In this work, we report a novel, to the best of our knowledge, sparse SPI scheme and corresponding reconstruction algorithm to image target scenes at above 1 K resolution with reduced measurements. Specifically, we first analyze the statistical importance ranking of Fourier coefficients for natural images. Then the sparse sampling with a polynomially decending probability of the ranking is performed to cover a larger range of the Fourier spectrum than non-sparse sampling. The optimal sampling strategy with suitable sparsity is summarized for the best performance. Next, a lightweight deep distribution optimization (D2O) algorithm is introduced for large-scale SPI reconstruction from sparsely sampled measurements instead of a conventional inverse Fourier transform (IFT). The D2O algorithm empowers robustly recovering sharp scenes at 1 K resolution within 2 s. A series of experiments demonstrate the technique's superior accuracy and efficiency.

Accelerating solid diffusion and suppressing phase transition in LiV3O8via calcium doping at lithium sites.

The phase transition of LiV3O8 from an α phase to a ß phase during the discharge/charge process leads to drastic structural change and rapid capacity decay, and the consequent sluggish Li+ solid-state diffusion results in a serious concentration polarization. Herein, Ca-doped LiV3O8 was rationally designed and synthesized to address these issues. The electrochemical behaviors of Ca-doped and undoped LiV3O8, together with their structural evolution and changes in the ion solid diffusion paths, are studied in detail. Calculations at the atomic scale have revealed that Ca doping effectively suppresses the undesired α-ß phase transition and stabilizes the structure of LiV3O8 during cycling. Moreover, the calcium dopant preferentially situated at lithium sites in LiV3O8 serves as a pillar to increase the interlayer distance and extend the electrochemically active (001) plane, and thus facilitates anisotropic Li+ diffusion. More importantly, the variable-cell Nudged-Elastic-Band (VCNEB) calculations indicate that the phase transformation was hindered by kinetic factors, not by thermodynamics. The dominant factors for the electrochemical performance of LiV3O8 were clarified, and valuable insights for LiV3O8 commercialization in lithium-ion batteries were provided.

Transient of scanning electron microscopic images for a buried microstructure in insulators.

We clarify the transient process and its mechanism of scanning electron microscope (SEM) images of a trench microstructure buried in insulators. First, interface charges of primary electrons trapped on the trench are derived from the charging model of a capacitor considering the electron beam induced current, and the surface potential is therefore assumed. The SEM signal current is then determined from its simplified relation with the surface potential. Calculated profiles of the secondary electron (SE) signal current and their time-evolution behaviors can well fit the transient of the experimental SEM images. Results show that the variation of the surface potential due to the transient interface charges and the effect of SE redistribution result in transients of the SEM imaging signal and the image width of the buried trench.