Optical remote sensing techniques, particularly those relying on visible optical sensors, are critical for Earth observations. However, atmospheric haze severely degrades image quality owing to light scattering effects. These effects often lead to clouds in the observed images, resulting in partial image degradation. To address the challenge of non-homogeneous image dehazing, we propose a model-inspired deep unfolding network (MiDUNet). The architecture of MiDUNet is strictly derived and inspired by a novel alternating minimization algorithm. During algorithm design, traditional image regularization terms are replaced by several nonlinear functions to align with neural network processing, increasing flexibility and applicability. Afterward, a Multi-scale Spatial frequency Residual Block (MSRB) is used to capture complex scene structures by fusing multi-scale spatial information and frequency information. With these spatial-frequency features and a Multi-path Feature Enhancement Block (MFEB), local and global information are obtained, which increases the robustness to non-homogeneous dehazing and image texture preservation. Numerical experiments with remote sensing images and natural images demonstrate the competitive advantages of MiDUNet in non-homogeneous dense haze scenarios.

Algorithms for learning a dictionary to sparsely represent a given dataset typically alternate between sparse coding and dictionary update stages. Methods for dictionary update aim to minimise expansion error by updating dictionary vectors and expansion coefficients given patterns of non-zero coefficients obtained in the sparse coding stage. We propose a block total least squares (BLOTLESS) algorithm for dictionary update. BLOTLESS updates a block of dictionary elements and the corresponding sparse coefficients simultaneously. In the error-free case, three necessary conditions for exact recovery are identified. Lower bounds on the number of training data are established so that the necessary conditions hold with high probability. Numerical simulations show that the bounds approximate well the number of training data needed for exact dictionary recovery. Numerical experiments further demonstrate several benefits of dictionary learning with BLOTLESS update compared with state-of-the-art algorithms especially when the amount of training data is small.

In computational ghost imaging, a spatial light modulator (SLM) can be used to modulate the light field. The relative locations and the number of light point pixels on an SLM affect the imaging quality. Usually, SLMs are two-dimensional arrays which are drawn uniformly or are randomly sparse. However, the patterns formed by a uniform array are periodic when the number of light point pixels is small, and the images formed by a random sparse array suffer from large background noise. In this paper, we introduce a coprime array based on the Eisenstein integer to optimize the light point pixel arrangement. A coprime array is widely used as a microwave radar receiving array, but less implemented in optics. This is the first time that a coprime array based on Eisenstein integer has been introduced in computational ghost imaging. A coprime array with this structure enhances the imaging quality when limited measurements are recorded, and it reduces background noise and avoids periodicity. All results are verified by numerical simulation.

The prediction of material properties is a crucial challenge in the design of new materials. Traditional methods based on either trial-and-error experiments or large-scale density functional theory calculations are known to possess various limitations. Although recent machine learning (ML) methods have shed light on resolving this problem efficiently, the majority of ML models consider only the local atomic environment while ignoring the nonlocal correlations between atoms. Indeed, even the periodic patterns of the crystal structures are not seriously considered. Consequently, these issues lead to an insufficient understanding of the feature information of atoms and bonds. In this study, we propose a crystal graph convolutional neural network based on edge convolution (EdgeConv) and correlative self-attention, namely, EdgeConv-Graph attention neural network (GANN). This network is able to efficiently extract atomic and bonding feature information, while effectively learning the importance weights of all neighbouring nodes. Numerical experiments predicting the electronic structural properties of metal–organic frameworks show that the developed model achieves state-of-the-art performance. Moreover, the proposed model was applied to predict the heat capacity and thermal decomposition temperature of material, demonstrating the ability of this method to effectively generalise multiscale prediction tasks.

The cavity-less electro-optic combs (EOCs), recognized for exceptional tunability, stability and high power, are a crucial enabler for the fields such as optical communications, precision measurement and metrology, and microwave photonics. This work systematically investigates the fundamental physical factors that govern the spectral flatness via ultrafast measurements and modeling simulations. The ultrafast analysis results demonstrate that, the finite effective modulation extinction ratio of the electro-optic intensity modulators will result in generation of coherent spectral components with identical frequencies but varying phases and amplitudes in ultrashort temporal scale, finally lead to remarkable spectral interference and further intensity fluctuations across the combs spectrum. Furthermore, the established mathematical relationship between the spectral flatness and the modulation extinction ratio of the intensity modulators exhibits a nonlinear dependence up to the third order. Cascading intensity modulators has been exploited to mitigate the spectral interference and improve the modulation extinction ratio, which has been verified by using home-made high sensitive autocorrelator and frequency-resolved optical gating (FROG), and finely spectral flatness of 0.54 dB among 11 lines has been achieved, which recognized for the first time that modulation extinction ratio related spectral interference phenomenon play a subtle role in EOCs generation. Furthermore, photonic analog-to-digital converters (PADCs) have been investigated and an obvious enhancement in signal-to-noise-and-distortion (SINAD) is achieved, These findings will provide crucial theoretical and experimental support for optimizing EOCs performance, and advance the development and application.