Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with handling glow or low-light conditions, resulting in either excessively dark visuals or unsuppressed glow outputs. In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions. To handle glow effects, our framework learns from the rendered glow pairs. Specifically, a light source aware network is proposed to detect light sources of night images, followed by the APSF (Angular Point Spread Function)-guided glow rendering. Our framework is then trained on the rendered images, resulting in glow suppression. Moreover, we utilize gradient-adaptive convolution, to capture edges and textures in hazy scenes. By leveraging extracted edges and textures, we enhance the contrast of the scene without losing important structural details. To boost low-light intensity, our network learns an attention map, then adjusted by gamma correction. This attention has high values on low-light regions and low values on haze and glow regions. Extensive evaluation on real nighttime haze images, demonstrates the effectiveness of our method. Our experiments demonstrate that our method achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13$\%$ on GTA5 nighttime haze dataset. Our data and code is available at: \url{https://github.com/jinyeying/nighttime_dehaze}.Comment: Accepted to ACM'MM2023, https://github.com/jinyeying/nighttime_dehaz

Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery

When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. While collecting real data pairs of flare-corrupted/flare-free images for training flare removal models is challenging, current methods utilize the direct-add approach to synthesize data. However, these methods do not consider automatic exposure and tone mapping in image signal processing pipeline (ISP), leading to the limited generalization capability of deep models training using such data. Besides, existing methods struggle to handle multiple light sources due to the different sizes, shapes and illuminance of various light sources. In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through convex combination, avoiding global illumination shifting and local over-saturation. Our strategy for recovering multiple light sources convexly averages the input and output of the neural network based on illuminance levels, thereby avoiding the need for a hard threshold in identifying light sources. We also contribute a new flare removal testing dataset containing the flare-corrupted images captured by ten types of consumer electronics. The dataset facilitates the verification of the generalization capability of flare removal methods. Extensive experiments show that our solution can effectively improve the performance of lens flare removal and push the frontier toward more general situations.Comment: ICCV 202

Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise ($1*1$) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10$\sim$1/100 network parameters and computational cost while achieving comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201

A Review of Remote Sensing Image Dehazing.

Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated

Switching GAN-based Image Filters to Improve Perception for Autonomous Driving

Autonomous driving holds the potential to increase human productivity, reduce accidents caused by human errors, allow better utilization of roads, reduce traffic accidents and congestion, free up parking space and provide many other advantages. Perception of Autonomous Vehicles (AV) refers to the use of sensors to perceive the world, e.g. using cameras to detect and classify objects. Traffic scene understanding is a key research problem in perception in autonomous driving, and semantic segmentation is a useful method to address this problem. Adverse weather conditions are a reality that AV must contend with. Conditions like rain, snow, haze, etc. can drastically reduce visibility and thus affect computer vision models. Models for perception for AVs are currently designed for and tested on predominantly ideal weather conditions under good illumination. The most complete solution may be to have the segmentation networks be trained on all possible adverse conditions. Thus a dataset to train a segmentation network to make it robust to rain would need to have adequate data that cover these conditions well. Moreover, labeling is an expensive task. It is particularly expensive for semantic segmentation, as each object in a scene needs to be identified and each pixel annotated in the right class. Thus, the adverse weather is a challenging problem for perception models in AVs. This thesis explores the use of Generative Adversarial Networks (GAN) in order to improve semantic segmentation. We design a framework and a methodology to evaluate the proposed approach. The framework consists of an Adversity Detector, and a series of denoising filters. The Adversity Detector is an image classifier that takes as input clear weather or adverse weather scenes, and attempts to predict whether the given image contains rain, or puddles, or other conditions that can adversely affect semantic segmentation. The filters are denoising generative adversarial networks that are trained to remove the adverse conditions from images in order to translate the image to a domain the segmentation network has been trained on, i.e. clear weather images. We use the prediction from the Adversity Detector to choose which GAN filter to use. The methodology we devise for evaluating our approach uses the trained filters to output sets of images that we can then run segmentation tasks on. This, we argue, is a better metric for evaluating the GANs than similarity measures such as SSIM. We also use synthetic data so we can perform systematic evaluation of our technique. We train two kinds of GANs, one that uses paired data (CycleGAN), and one that does not (Pix2Pix). We have concluded that GAN architectures that use unpaired data are not sufficiently good models for denoising. We train the denoising filters using the other architecture and we found them easy to train, and they show good results. While these filters do not show better performance than when we train our segmentation network with adverse weather data, we refer back to the point that training the segmentation network requires labelled data which is expensive to collect and annotate, particularly for adverse weather and lighting conditions. We implement our proposed framework and report a 17\% increase in performance in segmentation over the baseline results obtained when we do not use our framework

SEPARATING LAYERS IN IMAGES AND ITS APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH