Reliable image dehazing by NeRF.

Image dehazing is a typical low-level visual task. With the continuous improvement of network performance and the introduction of various prior knowledge, the ability of image dehazing is becoming stronger. However, the existing dehazing methods have problems such as the inability to obtain real shooting datasets, unreliable dehazing processes, and the difficulty to deal with complex lighting scenes. To solve these problems, we propose a new haze model combining the optical scattering model and the computer graphics rendering. Based on the new haze model, we propose a high-quality and widely applicable dehazing dataset generation pipeline that does not require paired-data training and prior knowledge. We reconstruct the three-dimensional fog space with array camera and remove haze by thresholding voxel deletion. We use the Unreal Engine 5 to generate simulation datasets and the real shooting in laboratory to verify the effectiveness and the reliability of our generation pipeline. Through our pipeline, we can obtain wonderful dehaze results and dehaze datasets under various complex outdoors lighting conditions. We also propose a dehaze dataset enhancement method based on voxel control. Our pipeline and data enhancement are suitable for the latest algorithm model, these solutions can obtain better visual effects and objective indicators.

Wavelength encoding spectral imaging based on the combination of deeply learned filters and an RGB camera.

Hyperspectral imaging is a critical tool for gathering spatial-spectral information in various scientific research fields. As a result of improvements in spectral reconstruction algorithms, significant progress has been made in reconstructing hyperspectral images from commonly acquired RGB images. However, due to the limited input, reconstructing spectral information from RGB images is ill-posed. Furthermore, conventional camera color filter arrays (CFA) are designed for human perception and are not optimal for spectral reconstruction. To increase the diversity of wavelength encoding, we propose to place broadband encoding filters in front of the RGB camera. In this condition, the spectral sensitivity of the imaging system is determined by the filters and the camera itself. To achieve an optimal encoding scheme, we use an end-to-end optimization framework to automatically design the filters' transmittance functions and optimize the weights of the spectral reconstruction network. Simulation experiments show that our proposed spectral reconstruction network has excellent spectral mapping capabilities. Additionally, our novel joint wavelength encoding imaging framework is superior to traditional RGB imaging systems. We develop the deeply learned filter and conduct actual shooting experiments. The spectral reconstruction results have an attractive spatial resolution and spectral accuracy.

Nighttime Image Dehazing by Render.

Nighttime image dehazing presents unique challenges due to the unevenly distributed haze caused by the color change of artificial light sources. This results in multiple interferences, including atmospheric light, glow, and direct light, which make the complex scattering haze interference difficult to accurately distinguish and remove. Additionally, obtaining pairs of high-definition data for fog removal at night is a difficult task. These challenges make nighttime image dehazing a particularly challenging problem to solve. To address these challenges, we introduced the haze scattering formula to more accurately express the haze in three-dimensional space. We also proposed a novel data synthesis method using the latest CG textures and lumen lighting technology to build scenes where various hazes can be seen clearly through ray tracing. We converted the complex 3D scattering relationship transformation into a 2D image dataset to better learn the mapping from 3D haze to 2D haze. Additionally, we improved the existing neural network and established a night haze intensity evaluation label based on the idea of optical PSF. This allowed us to adjust the haze intensity of the rendered dataset according to the intensity of the real haze image and improve the accuracy of dehazing. Our experiments showed that our data construction and network improvement achieved better visual effects, objective indicators, and calculation speed.

Temperature measurements in the freezing supercooled water droplet by utilizing molecular tagging thermometry technique.

In the present study, we report novel methods to achieve accurate temperature measurements inside the water droplet at its supercooled state as well as during its freezing process. The temperature measurements were based on the molecular tagging thermometry technique. In order to maintain the nonfreezing state of the phosphorescent tracer complex aqueous solution at a subfreezing temperature, a double-layer temperature control container was designed and fabricated. Then, the calibration between the lifetime and temperature of the phosphorescent tracer complex aqueous solution from 7.5 °C to as low as -6.0 °C was carefully performed. Then, the ice fraction (f) was applied to revise the calibration curve for the ice-liquid mixture. The results indicated that the calibration curve for the pure liquid was suitable for the temperature measurements of the pure phosphorescent triplex solution at the supercooling state, while the revised calibration curve for the ice-liquid mixture was appropriate for the temperature measurements in the water droplet during its freezing stage.

Quantification of unsteady heat transfer and phase changing process inside small icing water droplets.

We report progress made in our recent effort to develop and implement a novel, lifetime-based molecular tagging thermometry (MTT) technique to quantify unsteady heat transfer and phase changing process inside small icing water droplets pertinent to wind turbine icing phenomena. The lifetime-based MTT technique was used to achieve temporally and spatially resolved temperature distribution measurements within small, convectively cooled water droplets to quantify unsteady heat transfer within the small water droplets in the course of convective cooling process. The transient behavior of phase changing process within small icing water droplets was also revealed clearly by using the MTT technique. Such measurements are highly desirable to elucidate underlying physics to improve our understanding about important microphysical phenomena pertinent to ice formation and accreting process as water droplets impinging onto wind turbine blades.