In this paper, we propose FreqFormer, a frequency-enhanced framework for face super-resolution (FSR) that synergizes spectral decomposition and dynamic feature modulation to address Transformers' inherent low-frequency bias. Unlike existing methods, FreqFormer leverages Empirical Mode Decomposition (EMD) to decompose high-resolution (HR) images into hierarchical Intrinsic Mode Functions (IMFs), providing explicit frequency anchors for progressive high-frequency recovery. Simultaneously, a lightweight prompt module dynamically injects degradation-aware textures into Transformer blocks through learnable feature interaction, compensating for real-world distortions. This work establishes a novel framework that bridges spectral fidelity and adaptive learning, significantly advancing FSR toward high-frequency-accurate restoration. Experiments on CelebA and Helen datasets demonstrate FreqFormer's superiority, which achieves 22.42 dB PSNR on $16\times$ SR ($8\times 8 \rightarrow 128\times 128$) with higher high-frequency energy preservation than state-of-the-art methods. The framework's parameter efficiency and real-time performance enable practical deployment in security and biometric systems.

Gaussian Splatting (GS) methods, including 3DGS and 2DGS, have demonstrated significant effectiveness in real-time novel view synthesis (NVS), establishing themselves as a key technology in the field of computer graphics. However, GS-based methods still face challenges in rendering high-quality image details. Even when utilizing advanced frameworks, their outputs may display significant rendering artifacts when relying solely on a few input views, such as noise and blurriness. A reasonable approach is to conduct post-processing in order to restore clear details. Therefore, we propose GaussianEnhancer, a general GS-agnostic post-processor that employs a degradation-driven view blending method to improve the rendering quality of GS models. Specifically, we design a degradation modeling method tailored to the GS style and construct a large-scale training dataset to effectively simulate the native rendering artifacts of GS, enabling efficient training. In addition, we present a spatial information fusion framework that includes view fusion and depth modulation modules. This framework successfully integrates related images and leverages depth information from the target image to enhance rendering details. Through our GaussianEnhancer, we effectively eliminate the rendering artifacts of GS models and generate highly realistic image details. Based on GaussianEnhancer, we introduce GaussianEnhancer++, which features an enhanced GS-style degradation simulator, leading to improved enhancement quality. Furthermore, GaussianEnhancer++ can generate ultra-high-resolution outputs from noisy low resolution GS rendered images by leveraging the symmetry between high-resolution and low resolution images. Extensive experiments demonstrate the excellent restoration ability of GaussianEnhancer++ on various novel view synthesis benchmarks.

Novel view synthesis and 3D reconstruction have been extensively studied. Three-dimensional Gaussian Splatting (3DGS) has gained popularity due to its rapid training and real-time rendering capabilities. However, RGB imaging is highly dependent on ideal illumination conditions. In low-light situations such as at night or in the presence of occlusions, RGB images often suffer from blurred contours or even complete failure in imaging, which severely restricts the application of 3DGS in such scenarios. Thermal imaging technology, on the other hand, serves as an effective complement. Thermal images are solely influenced by heat sources and are immune to illumination conditions. This unique property enables them to clearly identify the contour information of objects in low-light environments. Nevertheless, thermal images exhibit significant limitations in presenting texture details due to their sensitivity to temperature variations rather than surface texture features. To capitalize on the strengths of both, we propose thermal geometrically accurate Gaussian Splatting (TGA-GS), a novel Gaussian Splatting model. TGA-GS is designed to leverage RGB and thermal information to generate high-quality meshes in low-light conditions. Meanwhile, given low-resolution thermal images and low-light RGB images as inputs, our method can generate high-resolution thermal and RGB images from novel viewpoints. Moreover, we also provide a real thermal imaging dataset captured with a handheld thermal infrared camera. This not only enriches the information content of the images but also provides a more reliable data basis for subsequent computer vision tasks in low-light scenarios.

The molecular structure characteristics of thermoelectric materials are important attributes that affect thermoelectric performance. Under the conventional standard, the molecular structure of thermoelectric materials includes a variety of spatial layering methods based on the positive and negative charges in the layer, electronegativity, and layer distance. According to the results of different structure stratification, we used machine learning methods to explore the relationship between molecular structure stratification and the thermoelectric properties of materials from the perspectives of element composition, atomic information, electron orbits, and energy distribution. Through the ensemble machine learning method to predict the Seebeck coefficient, conductivity, and power factor of thermoelectric materials, the feature selection and feature extraction of material stratification information can be carried out. Combined with the different eigenvector groups corresponding to the delamination results, we deeply excavate the relationship between the material delamination structure information and the thermoelectric properties and demonstrate the importance of the material delamination information on the thermoelectric properties from multiple aspects.