This work presents an effective depth-consistency self-prompt Transformer for image dehazing. It is motivated by an observation that the estimated depths of an image with haze residuals and its clear counterpart vary. Enforcing the depth consistency of dehazed images with clear ones, therefore, is essential for dehazing. For this purpose, we develop a prompt based on the features of depth differences between the hazy input images and corresponding clear counterparts that can guide dehazing models for better restoration. Specifically, we first apply deep features extracted from the input images to the depth difference features for generating the prompt that contains the haze residual information in the input. Then we propose a prompt embedding module that is designed to perceive the haze residuals, by linearly adding the prompt to the deep features. Further, we develop an effective prompt attention module to pay more attention to haze residuals for better removal. By incorporating the prompt, prompt embedding, and prompt attention into an encoder-decoder network based on VQGAN, we can achieve better perception quality. As the depths of clear images are not available at inference, and the dehazed images with one-time feed-forward execution may still contain a portion of haze residuals, we propose a new continuous self-prompt inference that can iteratively correct the dehazing model towards better haze-free image generation. Extensive experiments show that our method performs favorably against the state-of-the-art approaches on both synthetic and real-world datasets in terms of perception metrics including NIQE, PI, and PIQE.
Xi'an Jiaotong University（西安交通大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Fog and haze are weathers with low visibility which are adversarial to the driving safety of intelligent vehicles equipped with optical sensors like cameras and LiDARs. Therefore image dehazing for perception enhancement and haze image synthesis for testing perception abilities are equivalently important in the development of such autonomous driving systems. From the view of image translation, these two problems are essentially dual with each other, which have the potentiality to be solved jointly. In this paper, we propose an unsupervised Image-to-Image Translation framework based on Variatio…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should localize and recover affected regions while ensuring consistency between recovered and its neighboring regions. However owing to fixed receptive field of convolutional kernels and non uniform haze distribution, assuring consistency between regions is difficult. In this paper, we utilize an encoder-decoder based network architecture to perform the task of dehazing and integrate an spatially aware channe…
Xidian University（西安电子科技大学）；Shandong Normal University（山东师范大学）；Northwestern Polytechnical University（西北工业大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the research filed of singe image dehazing. To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios. Specifically, the d…
Tianjin University（天津大学）；University of Cambridge（剑桥大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；The Hong Kong Polytechnic University（香港理工大学）；Sun Yat-sen University（中山大学）
Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing methods. To address this issue, we propose a novel image dehazing framework collaborating with unlabeled real data. First, we develop a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps, i.e. the latent haze-free image, the transmission map, and the global atmospheric light estimate, respecting the physical model of a haze process. Our DID-Net predicts …
The Intricate Dance of Prompt Complexity, Quality, Diversity, and Consistency in T2I Models
FAIR at Meta - Montréal（Meta - Montréal 的 FAIR）；Mila - Québec AI Institute（魁北克 AI 院）；McGill University（麦吉尔大学）；Canada CIFAR AI Chair（加拿大 CIFAR 人工智能 chair）
TIT-Score: Evaluating Long-Prompt Based Text-to-Image Alignment via Text-to-Image-to-Text Consistency
Institute of Image Communication and Network Engineering（图像通信与网络工程研究所）；MoE Key Lab of Artificial Intelligence, AI Institute（人工智能关键实验室）；Shanghai Jiao Tong University（上海交通大学）
Huazhong University of Science and Technology（华中科技大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Image dehazing using learning-based methods has achieved state-of-the-art performance in recent years. However, most existing methods train a dehazing model on synthetic hazy images, which are less able to generalize well to real hazy images due to domain shift. To address this issue, we propose a domain adaptation paradigm, which consists of an image translation module and two image dehazing modules. Specifically, we first apply a bidirectional translation network to bridge the gap between the synthetic and real domains by translating images from one domain to another. And then, we use images…
We propose a learning-based multi-view stereo (MVS) method in scattering media, such as fog or smoke, with a novel cost volume, called the dehazing cost volume. Images captured in scattering media are degraded due to light scattering and attenuation caused by suspended particles. This degradation depends on scene depth; thus, it is difficult for traditional MVS methods to evaluate photometric consistency because the depth is unknown before three-dimensional (3D) reconstruction. The dehazing cost volume can solve this chicken-and-egg problem of depth estimation and image restoration by computin…
In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training. That is, we train the network by feeding clean and hazy images in an unpaired manner. Moreover, the proposed approach does not rely on estimation of the atmospheric scattering model parameters. Our method enhances CycleGAN formulation by combining cycle-consistency and perceptual losses in order to improve the quality of textural information recovery and generate visually better haze-free images. Typica…
Visual Prompting for One-shot Controllable Video Editing without Inversion
Singapore University of Technology and Design（新加坡科技设计大学）；Wuhan University（武汉大学）；Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore（新加坡资讯与通信研究院）
LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization
CNRS（法国国家科学研究中心）；Univ. Bordeaux（波尔多大学）；INRIA（法国国家信息与自动化研究所）；Bordeaux INP（波尔多国立高等工程技术学院）；Heriot-Watt University（赫瑞瓦特大学）；Maxwell Institute for Mathematical Sciences（麦克斯韦数学科学研究所）
When Schrödinger Bridge Meets Real-World Image Dehazing with Unpaired Training
Rocket Force University of Engineering（火箭军工程大学）；MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition, University of Science and Technology of China（脑启发智能感知与认知教育部重点实验室，中国科学技术大学）
Remote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects
School of Artificial Intelligence and Computer Science（人工智能与计算机科学学院）；Josef Kittler Research Institute on Artificial Intelligence（乔塞夫·基特勒人工智能研究所）；School of Electronic Engineering（电子工程学院）；College of Artificial Intelligence（人工智能学院）；Aerospace Information Research Institute（航天信息研究所）；Department of Computer Science（计算机科学系）
School of Engineering Science, University of Science and Technology of China（中国科学技术大学工程科学学院）；College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学机电学院）；School of Data Science, University of Science and Technology of China（中国科学技术大学数据科学学院）
Central South University（中南大学）；Beijing Jiaotong University（北京交通大学）
Image-to-image translation based on generative adversarial network (GAN) has achieved state-of-the-art performance in various image restoration applications. Single image dehazing is a typical example, which aims to obtain the haze-free image of a haze one. This paper concentrates on the challenging task of single image dehazing. Based on the atmospheric scattering model, a novel model is designed to directly generate the haze-free image. The main challenge of image dehazing is that the atmospheric scattering model has two parameters, i.e., transmission map and atmospheric light. When they are…
Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first and estimating depth subsequently from haze-free scenes. This way, the inner connections between colored haze and scene depth are lost. In this paper, we propose a real-time transformer for simultaneous single image Depth Estimation and Haze Removal (DEHRFormer). DEHRFormer consists of a single encoder and two task-specific decoders. The transformer decoders with learnable queries are designed to decode coupling feat…
