Deep Video Restoration for Under-Display Camera

Images or videos captured by the Under-Display Camera (UDC) suffer from severe degradation, such as saturation degeneration and color shift. While restoration for UDC has been a critical task, existing works of UDC restoration focus only on images. UDC video restoration (UDC-VR) has not been explored in the community. In this work, we first propose a GAN-based generation pipeline to simulate the realistic UDC degradation process. With the pipeline, we build the first large-scale UDC video restoration dataset called PexelsUDC, which includes two subsets named PexelsUDC-T and PexelsUDC-P corresponding to different displays for UDC. Using the proposed dataset, we conduct extensive benchmark studies on existing video restoration methods and observe their limitations on the UDC-VR task. To this end, we propose a novel transformer-based baseline method that adaptively enhances degraded videos. The key components of the method are a spatial branch with local-aware transformers, a temporal branch embedded temporal transformers, and a spatial-temporal fusion module. These components drive the model to fully exploit spatial and temporal information for UDC-VR. Extensive experiments show that our method achieves state-of-the-art performance on PexelsUDC. The benchmark and the baseline method are expected to promote the progress of UDC-VR in the community, which will be made public

A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal

Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vision area. The early face restoration methods mainly use statistic priors and degradation models, which are difficult to meet the requirements of real-world applications in practice. In recent years, face restoration has witnessed great progress after stepping into the deep learning era. However, there are few works to study deep learning-based face restoration methods systematically. Thus, this paper comprehensively surveys recent advances in deep learning techniques for face restoration. Specifically, we first summarize different problem formulations and analyze the characteristic of the face image. Second, we discuss the challenges of face restoration. Concerning these challenges, we present a comprehensive review of existing FR methods, including prior based methods and deep learning-based methods. Then, we explore developed techniques in the task of FR covering network architectures, loss functions, and benchmark datasets. We also conduct a systematic benchmark evaluation on representative methods. Finally, we discuss future directions, including network designs, metrics, benchmark datasets, applications,etc. We also provide an open-source repository for all the discussed methods, which is available at https://github.com/TaoWangzj/Awesome-Face-Restoration.Comment: 21 pages, 19 figure

The validity of the Physical Literacy in Children Questionnaire in children aged 4 to 12

Background: Given the growing evidence on the health benefits associated with physical literacy (PL), it is necessary to develop sound measures to assess the levels of PL in children. The Physical Literacy in Children Questionnaire (PL-C Quest) is the first self-report pictorial-based scale to assess children’s perceived PL. It has good validity and reliability in Australian children aged 7 to 12 years, but little is known in younger children and in other cultural contexts. The aim of this study was to examine the validity and reliability in an expanded age range. Methods: A total of 1,870 Chinese children (girls, n = 871; 46.6%), aged 4 to 12 years (M = 8.07 ± 2.42) participated in validity testing. Structural equation modeling with the Weighted Least Squares with Mean and Variance approach was used to assess construct validity. The hypothesized theoretical model used the 30 items and four hypothesized factors: physical, psychological, social and cognitive capabilities. Multigroup confirmatory factor analysis was used to assess sex and age group (4–6 years, 7–9 years and 10–12 years) measurement invariance. Internal consistency analyses were conducted using polychoric alpha. A random subsample (n = 262) was selected to determine test–retest reliability using Intra-Class Correlations (ICC). Results: All items except one (moving with equipment–skateboarding) loaded on sub-domains with λ > 0.45. The hypothesized model had a good fit (CFI = 0.954, TLI = 0.950, RMSEA = 0.042), with measurement equivalence across sex and age groups separately. Internal consistency values were good to excellent (overall: α = 0.94; physical: α = 0.86; psychological: α = 0.83; social: α = 0.81; cognitive: α = 0.86). Test–retest reliability was adequate to excellent (overall: ICC = 0.90, physical: ICC = 0.86, psychological: ICC = 0.75, social: ICC = 0.71, cognitive: ICC = 0.72).Conclusion: The Chinese version of the PL-C Quest is valid and reliable for testing the self-reported PL of Chinese children aged 4 to 12. This study provides the first evidence of validity for this tool in children aged 4–6 years and also evidence that the PL-C Quest would be a meaningful instrument to assess PL in Chinese children

D₂-Filled Hollow-Core Fiber Gas Raman Laser at 2.15 μm

Fiber lasers around 2 µm band have attractive applications, such as coherent detecting, material processing, pump source for mid-IR lasers based on nonlinear frequency shift, etc. Fiber gas Raman lasers (FGRLs) based on the stimulated Raman scattering of the gas molecules filled in the hollow-core fibers (HCFs) have been proved an efficient method to enrich the wavelengths of fiber lasers. In this paper, we demonstrated a deuterium-filled fiber gas Raman laser working at 2147 nm. The pump laser is directly coupled into the HCF through the fusion splice between the HCF and the solid-core fiber. By adjusting the pressure, fiber length as well as the repetition frequency of the 1971 nm pump laser, a maximum average Raman power of ~2.57 W was obtained, with corresponding efficiency of ~40%. This work provides a simple and compact configuration for 2.1 µm fiber lasers, which is significant for their application