Measuring atmospheric scattering from digital images of urban scenery using temporal polarization-based vision

Suspended atmospheric particles (particulate matter) are a form of air pollution that visually degrades urban scenery and is hazardous to human health and the environment. Current environmental monitoring devices are limited in their capability of measuring average particulate matter (PM) over large areas. Quantifying the visual effects of haze in digital images of urban scenery and correlating these effects to PM levels is a vital step in more practically monitoring our environment. Current image haze extraction algorithms remove all the haze from the scene and hence produce unnatural scenes for the sole purpose of enhancing vision. We present two algorithms which bridge the gap between image haze extraction and environmental monitoring. We provide a means of measuring atmospheric scattering from images of urban scenery by incorporating temporal knowledge. In doing so, we also present a method of recovering an accurate depthmap of the scene and recovering the scene without the visual effects of haze. We compare our algorithm to three known haze removal methods from the perspective of measuring atmospheric scattering, measuring depth and dehazing. The algorithms are composed of an optimization over a model of haze formation in images and an optimization using the constraint of constant depth over a sequence of images taken over time. These algorithms not only measure atmospheric scattering, but also recover a more accurate depthmap and dehazed image. The measurements of atmospheric scattering this research produces, can be directly correlated to PM levels and therefore pave the way to monitoring the health of the environment by visual means. Accurate atmospheric sensing from digital images is a challenging and under-researched problem. This work provides an important step towards a more practical and accurate visual means of measuring PM from digital images

A Study of Atmospheric Particles Removal in a Low Visibility Outdoor Single Image

Maximum limit of human visibility without the assistance of equipment is 1000 m based on International Commission on Illumination. The use of camera in the outdoor for the purpose of navigation, monitoring, remote sensing and robotic movement sometimes may yield images that are interrupted by haze, fog, smoke, steam and water drops. Fog is the random movement of water drops in the air that normally exists in the early morning. This disorder causes a differential image observed experiences low contrast, obscure, and difficult to identify targets. Analysis of the interference image can restore damaged image as a result of obstacles from atmospheric particles or drops of water during image observation. Generally, images with atmospheric particles contain a homogeneous texture like brightness and a heterogeneous texture which is the object that exists in the atmosphere. Pre-processing method based on the dark channel prior statistical measure of contrast vision and prior knowledge, still produces good image quality but less effective to overcome Halo problem or ring light, and strong lighting. This study aims to propel the development of machine vision industry aimed at navigation or monitoring for ground transportation, air or sea

An Efficient Edge Detection Technique for Hazy Images using DCP

Images of outdoor areas are typically degraded in quality by its turbid medium in the nature such as haze, fog and smoke. The absorption and scattering of light on such kind of images effects the quality of the image. The degraded images will loss the contrast and color artifacts from the original image. Edge detection is another challenging issue on such kinds of degraded images. There are several research works are under progress to reduce the haze exists in the image. Although haze removal techniques will reduce the haze present in the image, the results of those techniques were dropped the natural look of the original image as penalty. We proposed an effective way of finding the edges from the hazy images. Firstly, a dark channel prior method is used to eliminate the unwanted haze from the original image. The statistics shows that this method effectively works for the images taken in an outdoor hazy environment. The key observation of this method is that at least one color channel is having a minimum intensity value in a local patch. The results shows that results of this method have a good results compared to other contrast improvement techniques. Secondly we have applied the Sobel edge detection operator to find the edges of the resultant image

Multi-Scale Fusion of Enhanced Hazy Images Using Particle Swarm Optimization and Fuzzy Intensification Operators

Dehazing from a single image is still a challenging task, where the thickness of the haze depends on depth information. Researchers focus on this area by eliminating haze from the single image by using restoration techniques based on haze image model. Using haze image model, the haze is eliminated by estimating atmospheric light, transmission, and depth. A few researchers have focused on enhancement based methods for eliminating haze from images. Enhancement based dehazing algorithms will lead to saturation of pixels in the enhanced image. This is due to assigning fixed values to the parameters used to enhance an image. Therefore, the enhancement based methods fail in the proper tuning of the parameters. This can be overcome by optimizing the parameters that are used to enhance the images. This paper describes the research work carried to derive two enhanced images from a single input hazy image using particle swarm optimization and fuzzy intensification operators. The two derived images are further fused using multi-scale fusion technique. The objective evaluation shows that the entropy of the haze eliminated images is comparatively better than the state-of-the-art algorithms. Also, the fog density is measured using an evaluator known as fog aware density evaluator (FADE), which considers all the statistical parameters to differentiate a hazy image from a highly visible natural image. Using this evaluator we found that the density of the fog is less in our proposed method when compared with enhancement based algorithms used to eliminate haze from images

Non-aligned supervision for Real Image Dehazing

Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in misaligned hazy and clear image pairs. In this paper, we propose a non-aligned supervision framework that consists of three networks - dehazing, airlight, and transmission. In particular, we explore a non-alignment setting by utilizing a clear reference image that is not aligned with the hazy input image to supervise the dehazing network through a multi-scale reference loss that compares the features of the two images. Our setting makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To demonstrate this, we have created a new hazy dataset called "Phone-Hazy", which was captured using mobile phones in both rural and urban areas. Additionally, we present a mean and variance self-attention network to model the infinite airlight using dark channel prior as position guidance, and employ a channel attention network to estimate the three-channel transmission. Experimental results show that our framework outperforms current state-of-the-art methods in the real-world image dehazing. Phone-Hazy and code will be available at https://github.com/hello2377/NSDNet