Zongxi Yu

Zongxi Yu, Cyprien Tamekue, Ruiqi Chen et al.

This article presents a scalable implementation of nonlinear Gramian-based control synthesis for control-affine systems, including a minimum energy control construction. These synthesis advances are achieved by addressing key computational bottlenecks inherent to iterative synthesis map formulations, yielding a computational scheme that exhibits rapid convergence and high-precision. The efficacy of this synthesis framework is demonstrated across five canonical nonlinear control systems and 100-dimensional recurrent neural network models, including underactuated systems. Empirical scaling results further indicate that convergence is primarily governed by intrinsic system properties, such as nonlinearity and controllability, rather than by state-space dimensionality. This work provides a practical, scalable computational pathway for translating rigorous nonlinear synthesis theory into high-dimensional control applications.

Xiaolong Qian, Qi Jiang, Lei Sun et al.

Beyond the commonly recognized optical aberrations, the imaging performance of compact optical systems-including single-lens and metalens designs-is often further degraded by veiling glare caused by stray-light scattering from non-ideal optical surfaces and coatings, particularly in complex real-world environments. This compound degradation undermines traditional lens aberration correction yet remains underexplored. A major challenge is that conventional scattering models (e.g., for dehazing) fail to fit veiling glare due to its spatial-varying and depth-independent nature. Consequently, paired high-quality data are difficult to prepare via simulation, hindering application of data-driven veiling glare removal models. To this end, we propose VeilGen, a generative model that learns to simulate veiling glare by estimating its underlying optical transmission and glare maps in an unsupervised manner from target images, regularized by Stable Diffusion (SD)-based priors. VeilGen enables paired dataset generation with realistic compound degradation of optical aberrations and veiling glare, while also providing the estimated latent optical transmission and glare maps to guide the veiling glare removal process. We further introduce DeVeiler, a restoration network trained with a reversibility constraint, which utilizes the predicted latent maps to guide an inverse process of the learned scattering model. Extensive experiments on challenging compact optical systems demonstrate that our approach delivers superior restoration quality and physical fidelity compared with existing methods. These suggest that VeilGen reliably synthesizes realistic veiling glare, and its learned latent maps effectively guide the restoration process in DeVeiler. All code and datasets will be publicly released at https://github.com/XiaolongQian/DeVeiler.

Zongxi Yu, Xiaolong Qian, Shaohua Gao et al.

Achieving high-fidelity, compact RGBD imaging presents a dual challenge: conventional compact optics struggle with RGB sharpness across the entire depth-of-field, while software-only Monocular Depth Estimation (MDE) is an ill-posed problem reliant on unreliable semantic priors. While deep optics with elements like DOEs can encode depth, they introduce trade-offs in fabrication complexity and chromatic aberrations, compromising simplicity. To address this, we first introduce a novel bio-inspired all-spherical monocentric lens, around which we build the Bionic Monocentric Imaging (BMI) framework, a holistic co-design. This optical design naturally encodes depth into its depth-varying Point Spread Functions (PSFs) without requiring complex diffractive or freeform elements. We establish a rigorous physically-based forward model to generate a synthetic dataset by precisely simulating the optical degradation process. This simulation pipeline is co-designed with a dual-head, multi-scale reconstruction network that employs a shared encoder to jointly recover a high-fidelity All-in-Focus (AiF) image and a precise depth map from a single coded capture. Extensive experiments validate the state-of-the-art performance of the proposed framework. In depth estimation, the method attains an Abs Rel of 0.026 and an RMSE of 0.130, markedly outperforming leading software-only approaches and other deep optics systems. For image restoration, the system achieves an SSIM of 0.960 and a perceptual LPIPS score of 0.082, thereby confirming a superior balance between image fidelity and depth accuracy. This study illustrates that the integration of bio-inspired, fully spherical optics with a joint reconstruction algorithm constitutes an effective strategy for addressing the intrinsic challenges in high-performance compact RGBD imaging. Source code will be publicly available at https://github.com/ZongxiYu-ZJU/BMI.