Seeing Beyond Haze: Generative Nighttime Image Dehazing

Nighttime image dehazing is particularly challenging when dense haze and intense glow severely degrade or completely obscure background information. Existing methods often encounter difficulties due to insufficient background priors and limited generative ability, both essential for handling such conditions. In this paper, we introduce BeyondHaze, a generative nighttime dehazing method that not only significantly reduces haze and glow effects but also infers background information in regions where it may be absent. Our approach is developed on two main ideas: gaining strong background priors by adapting image diffusion models to the nighttime dehazing problem, and enhancing generative ability for haze- and glow-obscured scene areas through guided training. Task-specific nighttime dehazing knowledge is distilled into an image diffusion model in a manner that preserves its capacity to generate clean images. The diffusion model is additionally trained on image pairs designed to improve its ability to generate background details and content that are missing in the input image due to haze effects. Since generative models are susceptible to hallucinations, we develop our framework to allow user control over the generative level, balancing visual realism and factual accuracy. Experiments on real-world images demonstrate that BeyondHaze effectively restores visibility in dense nighttime haze.

NightRain: Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction

Existing deep-learning-based methods for nighttime video deraining rely on synthetic data due to the absence of real-world paired data. However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts. Motivated by this, we introduce NightRain, a novel nighttime video deraining method with adaptive-rain-removal and adaptive-correction. Our adaptive-rain-removal uses unlabeled rain videos to enable our model to derain real-world rain videos, particularly in regions affected by complex light effects. The idea is to allow our model to obtain rain-free regions based on the confidence scores. Once rain-free regions and the corresponding regions from our input are obtained, we can have region-based paired real data. These paired data are used to train our model using a teacher-student framework, allowing the model to iteratively learn from less challenging regions to more challenging regions. Our adaptive-correction aims to rectify errors in our model's predictions, such as over-saturation and color shifts. The idea is to learn from clear night input training videos based on the differences or distance between those input videos and their corresponding predictions. Our model learns from these differences, compelling our model to correct the errors. From extensive experiments, our method demonstrates state-of-the-art performance. It achieves a PSNR of 26.73dB, surpassing existing nighttime video deraining methods by a substantial margin of 13.7%.

Continuous-time Radar-inertial Odometry for Automotive Radars

We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that of camera and LiDAR sensors. Radar's robustness in such conditions and the increasing prevalence of radars on passenger vehicles motivate us to look at the use of radar for ego-motion estimation. A continuous-time trajectory representation is applied not only as a framework to enable heterogeneous and asynchronous multi-sensor fusion, but also, to facilitate efficient optimization by being able to compute poses and their derivatives in closed-form and at any given time along the trajectory. We compare our continuous-time estimates to those from a discrete-time radar-inertial odometry approach and show that our continuous-time method outperforms the discrete-time method. To the best of our knowledge, this is the first time a continuous-time framework has been applied to radar-inertial odometry.