Haze limits visibility and reduces image contrast in outdoor images. The degradation is different for every pixel and depends on the distance of the scene point from the camera. This dependency is expressed in the transmission coefficients, that control the scene attenuation and amount of haze in every pixel. Previous methods solve the single image dehazing problem using various patch-based priors. We, on the other hand, propose an algorithm based on a new, non-local prior. The algorithm relies on the assumption that colors of a haze-free image are well approximated by a few hundred distinct colors, that form tight clusters in RGB space. Our key observation is that pixels in a given cluster are often non-local, i.e., they are spread over the entire image plane and are located at different distances from the camera. In the presence of haze these varying distances translate to different transmission coefficients. Therefore, each color cluster in the clear image becomes a line in RGB space, that we term a haze-line. Using these haze-lines, our algorithm recovers both the distance map and the haze-free image. The algorithm is linear in the size of the image, deterministic and requires no training. It performs well on a wide variety of images and is competitive with other stateof-the-art methods.

/pdf/non-local-image-dehazing-2iixc4an79.pdf

Non-local Image Dehazing

We present a deep learning based method for low-light image enhancement. This problem is challenging due to the difficulty in handling various factors simultaneously including brightness, contrast, artifacts and noise. To address this task, we propose the multi-branch low-light enhancement network (MBLLEN). The key idea is to extract rich features up to different levels, so that we can apply enhancement via multiple subnets and finally produce the output image via multi-branch fusion. In this manner, image quality is improved from different aspects. Through extensive experiments, our proposed MBLLEN is found to outperform the state-of-art techniques by a large margin. We additionally show that our method can be directly extended to handle low-light videos.

MBLLEN: Low-Light Image/Video Enhancement Using CNNs.

The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fail in adverse weather where the sensory streams can be asymmetrically distorted. These rare ``edge-case'' scenarios are not represented in available datasets, and existing fusion architectures are not designed to handle them. To address this challenge we present a novel multimodal dataset acquired in over 10,000~km of driving in northern Europe. Although this dataset is the first large multimodal dataset in adverse weather, with 100k labels for lidar, camera, radar, and gated NIR sensors, it does not facilitate training as extreme weather is rare. To this end, we present a deep fusion network for robust fusion without a large corpus of labeled training data covering all asymmetric distortions. Departing from proposal-level fusion, we propose a single-shot model that adaptively fuses features, driven by measurement entropy. We validate the proposed method, trained on clean data, on our extensive validation dataset. Code and data are available here https://github.com/princeton-computational-imaging/SeeingThroughFog.

Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather

Getting to know low-light images with the Exclusively Dark dataset

Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light has seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark dataset such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i.e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing visualizations of both hand-crafted and learned features. Most importantly, we found that the effects of low-light reaches far deeper into the features than can be solved by simple "illumination invariance'". It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The Exclusively Dark dataset with its annotation is available at this https URL

Getting to Know Low-light Images with The Exclusively Dark Dataset

In this paper, we propose a new single image dehazing approach based on information fidelity and image entropy. The global atmospheric light is estimated by quadtree subdivision using transformed hazy images. Then, transmission is estimated by an objective function which is comprised of information fidelity and image entropy at non-overlapped sub-block regions. This is further refined by a Weighted Least Squares (WLS) optimization procedure to alleviate block artifacts. We compared performance of the proposed method with conventional methods to validate its effectiveness in an experiment.

Single image dehazing with image entropy and information fidelity

In this paper, a novel approach for noise reduction and enhancement of extremely low-light video is proposed. For noise removal, a motion adaptive temporal filtering based on a Kalman structured updating is presented. Dynamic range of denoised video is increased by adjustment of RGB histograms using Gamma correction with adaptive clipping thresholds. Finally, residual noise is removed using a nonlocal means (NLM) denoising filter. The proposed method works directly on the color filter array (CFA) raw video for achieving low memory consumption1.

A novel approach for denoising and enhancement of extremely low-light video

Images captured under hazy conditions have low contrast and poor color. This is primarily due to air-light which degrades image quality according to the transmission map. The approach to enhance these hazy images we introduce here is based on the `Dark-Channel Prior' method with image refinement by the `Weighted Least Square' based edge-preserving smoothing. Local contrast is further enhanced by multi-scale tone manipulation. The proposed method improves the contrast, color and detail for the entire image domain effectively. In the experiment, we compare the proposed method with conventional methods to validate performance.

Single image haze removal with WLS-based edge-preserving smoothing filter

Provided is a method of merging or fusing a visible image captured by a visible camera and an infrared (IR) image captured by an IR camera. The method includes: providing a first image of a subject captured by a first camera and a second image of the subject captured by a second camera; converting brightness of the second image based on brightness of the first image; and merging the first image and the second image having the converted brightness.

Apparatus and method for fusing images

This paper presents a new single image dehaze approach that uses a novel estimation of the atmospheric light and media transmission. Conventional dehaze methods often result in degraded images with low contrast and/or oversaturation of color in some regions. In order to mitigate these problems we use local atmospheric light and estimate the media transmission for each local region by using an objective function represented by modified saturation evaluation metric and intensity difference. Experimental results on a variety of outdoor haze images show that the proposed method achieves excellent restoration in terms of contrast, color fidelity and image visibility.

Dubok Park

Papers

Single image dehazing with image entropy and information fidelity

A novel approach for denoising and enhancement of extremely low-light video

Single image haze removal with WLS-based edge-preserving smoothing filter

Apparatus and method for fusing images

Single image haze removal using novel estimation of atmospheric light and transmission