While recent low-cost radar-camera approaches have shown promising results in multi-modal 3D object detection, both sensors face challenges from environmen- tal and intrinsic disturbances. Poor lighting or adverse weather conditions de- grade camera performance, while radar suffers from noise and positional ambigu- ity. Achieving robust radar-camera 3D object detection requires consistent perfor- mance across varying conditions, a topic that has not yet been fully explored. In this work, we first conduct a systematic analysis of robustness in radar-camera de- tection on five kinds of noises and propose RobuRCDet, a robust object detection model in bird’s eye view (BEV). Specifically, we design a 3D Gaussian Expan- sion (3DGE) module to mitigate inaccuracies in radar points, including position, Radar Cross-Section (RCS), and velocity. The 3DGE uses RCS and velocity priors to generate a deformable kernel map and variance for kernel size adjustment and value distribution. Additionally, we introduce a weather-adaptive fusion module, which adaptively fuses radar and camera features based on camera signal confi- dence. Extensive experiments on the popular benchmark, nuScenes, show that our RobuRCDet achieves competitive results in regular and noisy conditions. The source codes and trained models will be made available.

No-Reference Image Quality Assessment for distorted images has always been a challenging problem due to image content variance and distortion diversity. Previous IQA models mostly encode explicit single-quality features of synthetic images to obtain quality-aware representations for quality score prediction. However, performance decreases when facing real-world distortion and restored images from restoration models. The reason is that they do not consider the degradation factors of the low-quality images adequately. To address this issue, we first introduce the DRI method to obtain degradation vectors and quality vectors of images, which separately model the degradation and quality information of low-quality images. After that, we add the restoration network to provide the MOS score predictor with degradation information. Then, we design the Representation-based Semantic Loss (RS Loss) to assist in enhancing effective interaction between representations. Extensive experimental results demonstrate that the proposed method performs favorably against existing state-of-the-art models on both synthetic and real-world datasets. The source code will be released at https://github.com/Jingtong0527/DRI-IQA.

Deep learning methods have demonstrated state-of-the-art performance in image restoration, especially when trained on large-scale paired datasets. However, acquiring paired data in real-world scenarios poses a significant challenge. Unsupervised restoration approaches based on generative adversarial networks (GANs) offer a promising solution without requiring paired datasets. Yet, these GAN-based approaches struggle to surpass the performance of conventional unsupervised GAN-based frameworks without significantly modifying model structures or increasing the computational complexity. To address these issues, we propose a self-collaboration (SC) strategy for existing restoration models. This strategy utilizes information from the previous stage as feedback to guide subsequent stages, achieving significant performance improvement without increasing the framework's inference complexity. The SC strategy comprises a prompt learning (PL) module and a restorer ($Res$Res). It iteratively replaces the previous less powerful fixed restorer $\overline{Res}$Res¯ in the PL module with a more powerful $Res$Res. The enhanced PL module generates better pseudo-degraded/clean image pairs, leading to a more powerful $Res$Res for the next iteration. Our SC can significantly improve the $Res$Res 's performance by over 1.5 dB without adding extra parameters or computational complexity during inference. Meanwhile, existing self-ensemble (SE) and our SC strategies enhance the performance of pre-trained restorers from different perspectives. As SE increases computational complexity during inference, we propose a re-boosting module to the SC (Reb-SC) to improve the SC strategy further by incorporating SE into SC without increasing inference time. This approach further enhances the restorer's performance by approximately 0.3 dB. Additionally, we present a baseline framework that includes parallel generative adversarial branches with complementary "self-synthesis" and "unpaired-synthesis" constraints, ensuring the effectiveness of the training framework. Extensive experimental results on restoration tasks demonstrate that the proposed model performs favorably against existing state-of-the-art unsupervised restoration methods.

Rain in the dark poses significant challenges to deploying real-world applications such as autonomous driving, surveillance systems, and night photography. Existing low-light enhancement or deraining methods struggle to brighten low-light conditions and remove rain simultaneously. Cascade approaches, like “deraining followed by low-light enhancement” and vice versa, often result in problematic rain patterns or overly blurred and overexposed images. To address these challenges, we introduce a novel two-stage model called L2RIRNet, which innovatively integrates low-light enhancement and deraining into a single framework in real-world settings. Our model comprises two key components: a Dual Degradation Representation Network (DDR-Net) and a Restoration Network. The DDR-Net independently learns degradation representations for luminance effects in dark areas and rain patterns in light areas, which are constrained by dual degradation loss and have not been discussed in the previous methods. The Restoration Network restores the degraded image using a Fourier Detail Guidance (FDG) module, which focuses on texture details in frequency and spatial domains to inform the restoration process and leverages near-rainless detailed images. Furthermore, we contribute a dataset containing both synthetic and real-world low-light-rainy images. Extensive experiments demonstrate that our L2RIRNet performs favorably against existing methods in synthetic and complex real-world scenarios. All the code and dataset can be found inhttps://github.com/linxin0/Low_light_rainy.