Haze Removal Approaches Research Articles

Unsupervised single image dehazing — A contour approach

Small particles present in the air degrade the visual clarity of images due to light scattering phenomena caused by particles. This degradation of the image is in the context of attenuation of light intensity and poor contrast, which ultimately have an impact on the image’s quality. Thus, image dehazing is a necessity for better visualization and image analysis. The proposed method uses an unsupervised approach to dehaze the image without using any ground truth image or transmission map, which overcomes the necessity of a paired dataset and a true depth map. The majority of current state-of-the-art haze removal approaches observe color shifts in the sky region. By utilizing the contour process and RGB color plane data, the proposed innovative ”ContourDCP” technique can effectively identify the sky area. After applying the Dark Channel Prior (DCP) to obtain the initial transmission map, it is further reformulate through the contour method to achieve an accurate color representation of the sky area during the image dehazing process. We proposed atmospheric light estimation based on the haze concentration present in the Y-plane after conversion from RGB to YCbCr color space. These modified transmission map and atmospheric light are used in the atmospheric scattering model to recover the scene radiance. Performance evaluation of the method shows the robustness of the proposed method compared to existing state-of-the-art methods.

Wavelet-based Auto-Encoder for simultaneous haze and rain removal from images

Noise introduced due to weather can reduce the efficiency of computer vision applications as the visibility of the objects in images is greatly affected. Haze and rain are the most common weather conditions seen in nature. However, most of the algorithms found in the literature apply rain and haze removal approaches separately. To this end, in this paper, we propose a novel Wavelet-based deep Auto-encoder, called WAE, for simultaneously removing the haze and rain effects from images. The proposed network uses wavelet transformation and inverse wavelet transformation as an alternative to down-sampling and up-sampling operations, respectively, in order to add sparsity to the network. By training the model on both spatial and frequency domains, it learns non-stationary features that are found to be useful to remove haze and rain effects from images. The proposed model is tested on several rain and haze-affected image datasets, and it performs well in terms of standard evaluation metrics like structural similarity index measure and peak signal-to-noise ratio. The code can be found at : https://github.com/asfakali/WAE.git.

Recent advancement in haze removal approaches

Recent advancement in haze removal approaches

Enhancement and denoising method for low-quality MRI, CT images via the sequence decomposition Retinex model, and haze removal algorithm

The visibility and analyzability of MRI and CT images have a great impact on the diagnosis of medical diseases. Therefore, for low-quality MRI and CT images, it is necessary to effectively improve the contrast while suppressing the noise. In this paper, we propose an enhancement and denoising strategy for low-quality medical images based on the sequence decomposition Retinex model and the inverse haze removal approach. To be specific, we first estimate the smoothed illumination and de-noised reflectance in a successive sequence. Then, we apply a color inversion from 0-255 to the estimated illumination, and introduce a haze removal approach based on the dark channel prior to adjust the inverted illumination. Finally, the enhanced image is generated by combining the adjusted illumination and the de-noised reflectance. As a result, improved visibility is obtained from the processed images and inefficient or excessive enhancement is avoided. To verify the reliability of the proposed method, we perform qualitative and quantitative evaluation on five MRI datasets and one CT dataset. Experimental results demonstrate that the proposed method strikes a splendid balance between enhancement and denoising, providing performance superior to that of several state-of-the-art methods.

Real Time Night Vision Surveillance using Improved Dark Channel Prior

Videos taken under low lighting conditions usually result in severe loss of visibility and contrast and are uncomfortable for observation and analysis. Night vision cameras that cater to the needs are expensive and less versatile. To be cost effective and extract maximum information from videos taken in low lit conditions, video enhancing techniques must be used. Though there are many night vision enhancement techniques available in literature, this paper particularly emphasizes about Improved Dark Channel Prior algorithm and its results. This approach suits well for real time night video enhancement. It has been found that a pixel-wise inversion of a night video appears very similar to the video obtained during foggy days. The same idea of haze removal approach is used to boost the visual quality of night videos. An improved dark channel prior model is presented that is integrated with Gaussian Pyramid operators for local smoothing. The experimental results show that the proposed method can boost the perceptual quality of detailing in night videos.

Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids.

Model-based single image dehazing was widely studied due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model-based single image dehazing. In this paper, a dark direct attenuation prior (DDAP) is proposed to address the former problem. A novel haze line averaging is proposed to reduce the morphological artifacts caused by the DDAP which enables a weighted guided image filter with a smaller radius to further reduce the morphological artifacts while preserve the fine structure in the image. A multi-scale dehazing algorithm is then proposed to address the latter problem by adopting Laplacian and Gaussian pyramids to decompose the hazy image into different levels and applying different haze removal and noise reduction approaches to restore the scene radiance at the different levels. The resultant pyramid is collapsed to restore a haze-free image. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art dehazing algorithms.

Detail-preserving single nighttime image dehazing

Most existing dehazing approaches employ image priors such as a dark channel prior, which may be violated in the nighttime. In addition, these approaches perform dehazing directly on the original hazy image, which usually results in detail blurring and noise amplifying. To address both issues, an effective single nighttime image haze removal approach is presented. We first decompose the hazy image into a structure layer and a texture layer. Specifically, the dehazing operation is performed only on the structure layer to avoid detail loss and noise amplification. Due to the low frequency characteristics of the haze and the glow of artificial lights, the atmospheric veil is estimated on structure layer dehazing. Furthermore, to eliminate the nonuniform illumination effects, we present an ambient light estimation approach based on multiscale fusion. At the same time, the denoising is performed in the texture layer. Extensive experiments demonstrate that the proposed approach recovers a high-quality, haze-free, and noise-free image with vivid details.

A new haze removal approach for sky/river alike scenes based on external and internal clues

Sky/river alike areas are important parts of natural scenes. The haze of these areas will impact the vividness of natural scenes. More serious, once the haze occurs in navigation of flight and ship, it will threaten the safety. However, previous researches did not dehaze well for these areas. In this paper, a new haze removal approach is presented to improve the dehazing effect for the sky/river alike areas based on our new discovers. Inspired by the two discovers, we define two theoretical clues: the external boundary clue and the internal clue. And then a correction model is constructed to correct the dark channel values in the sky/river alike areas. Finally, an optimization solution is presented to solve this model. Both the visual experiment and the quantitative experiment show that proposed method outperforms the classical dark channel method and the deep learning method in sky/river alike areas.

Image dehazing using window-based integrated means filter

Image acquisition is generally susceptible to poor environmental conditions such as fog, smog, haze, etc. However, designing an efficient image dehazing technique is still an ill posed problem. Extensive review of the competitive haze removal approaches reveal that the texture preservation and computational speed are still a challenging issues. Therefore, in this paper, initially, a mask is utilized to decompose an input image into low and high frequency regions based on image gradient magnitude. Thereafter, a Gradient sensitive loss (GSL) is designed to obtain the depth information from an input hazy image. Thereafter, transmission map is refined by designing an efficient filter named as Window-based integrated means filter (WIMF). Finally, the restoration model is utilized to recover the hazy images. Experimental analysis reveals that the proposed dehazing technique achieves considerable results beyond the prototypes of the benchmarks. Additionally, the proposed technique outperforms the state-of-the-arts in single image dehazing approaches.

Perception Oriented Haze Image Definition Restoration by Basing on Physical Optics Model

Existing haze removal approaches have unreliable estimation of atmospheric light and transmission, which lead to imprecision color information recovery, gradient reversal artifacts, and halo effect. In this paper, a novel haze removal technique based on ambient light model is presented. Our technique applies canny edge detection algorithm which can locate the sky area accurately, and adopts binary-tree algorithm to obtain the radiance of atmospheric light A rapidly; then, an adaptive wiener filter is employed for modifying transmission map, and halo effect is further restricted in the sudden changed region of scene depth by the morphology method. Experimental results verify that the optimized approach has better robustness, and is superior to present methods.

Haze Removal Using Radial Basis Function Networks for Visibility Restoration Applications.

Restoration of visibility in hazy images is the first relevant step of information analysis in many outdoor computer vision applications. To this aim, the restored image must feature clear visibility with sufficient brightness and visible edges, while avoiding the production of noticeable artifacts. In this paper, we propose a haze removal approach based on the radial basis function (RBF) through artificial neural networks dedicated to effectively removing haze formation while retaining not only the visible edges but also the brightness of restored images. Unlike traditional haze-removal methods that consist of single atmospheric veils, the multiatmospheric veil is generated and then dynamically learned by the neurons of the proposed RBF networks according to the scene complexity. Through this process, more visible edges are retained in the restored images. Subsequently, the activation function during the testing process is employed to represent the brightness of the restored image. We compare the proposed method with the other state-of-the-art haze-removal methods and report experimental results in terms of qualitative and quantitative evaluations for benchmark color images captured in typical hazy weather conditions. The experimental results demonstrate that the proposed method is able to produce brighter and more vivid haze-free images with more visible edges than can the other state-of-the-art methods.

REVIEW ON VARIOUS HAZE REMOVAL TECHNIQUES

This paper represents that image defogging is commonly used in many outside working arrangements. The fog removal methods play significant role in various areas of vision processing. Haze detection and removal is a challenging task for improving the quality of digital images. In general, these images are captured at a huge distance from the visual sensor to given scene. Some climatic effects such as fog, smoke, dust etc reduce the quality of the received image. The long-term objective of this paper is to show the comparison between different haze removal approaches which illustrate the better quality results.

Haze Removal via Edge Weighted Pixel-to-Patch Fusion

One of the key issues in image dehazing is how to accurately estimate the transmission map using strong priors or assumptions. By far the most common prior adopted by existing haze removal approaches is dark channel prior (DCP). Despite the remarkable progress, the existing DCP-based methods induce misestimation of the transmission while preventing halo artifacts, and may cause distortions of the recovered haze-free images in both chromaticity and contrast. This because they fail to observe the fact that the edges in a color image do not necessarily correspond to the changes in depth; instead, they try to preserve all types of edges indiscriminately in the estimated transmission map, which does not accord with the property of a realistic transmission map. In order to address this issue, we propose an efficient method for haze removal under the guidance of the depth edges. The main contribution of our study is that we present a depth edge prior to obtain the depth edges from the hazy image, and then employ a pixel-to-patch fusion scheme weighted by the depth edges to estimate the transmission directly, which can preserve the sharp discontinuity at depth edges but smooth the surface texture in the rest regions of the transmission map. The experimental results show that our approach can obtain more accurate estimation of the transmission, and consequently restore better quality haze-free images in terms of less color distortion and high contrast.

Review: An Enhancement of Road Scenes Captured Images Using HDCP and Color Discriptor

The visibility of images of outdoor road scenes will generally become degraded when captured during inclement weather conditions. Drivers often turn on the headlights of their vehicles and streetlights are often activated, resulting in localized light sources in images capturing road scenes in these conditions. Additionally, sandstorms are also weather events that are commonly encountered when driving in some regions. A novel and effective haze removal approach to remedy problems caused by localized light sources and color shifts, which thereby achieves superior restoration results for single hazy images. The Road image degradation can cause problems for intelligent transportation systems such as traveling vehicle data recorders and traffic surveillance systems, which must operate under a wide range of weather conditions. The objective of this work is to implement the Road Scenes Captured by Intelligent Transportation Systems using Hybrid technique. To enhance the images using different filters and enhancement techniques.

An Advanced Visibility Restoration Algorithm for Single Hazy Images

Haze removal is the process by which horizontal obscuration is eliminated from hazy images captured during inclement weather. Images captured in natural environments with varied weather conditions frequently exhibit localized light sources or color-shift effects. The occurrence of these effects presents a difficult challenge for hazy image restoration, with which many traditional restoration methods cannot adequately contend. In this article, we present a new image haze removal approach based on Fisher's linear discriminant-based dual dark channel prior scheme in order to solve the problems associated with the presence of localized light sources and color shifts, and thereby achieve effective restoration. Experimental restoration results via qualitative and quantitative evaluations show that our proposed approach can provide higher haze-removal efficacy for images captured in varied weather conditions than can the other state-of-the-art approaches.

An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Intelligent Transportation Systems

The visibility of images of outdoor road scenes will generally become degraded when captured during inclement weather conditions. Drivers often turn on the headlights of their vehicles and streetlights are often activated, resulting in localized light sources in images capturing road scenes in these conditions. Additionally, sandstorms are also weather events that are commonly encountered when driving in some regions. In sandstorms, atmospheric sand has a propensity to irregularly absorb specific portions of a spectrum, thereby causing color-shift problems in the captured image. Traditional state-of-the-art restoration techniques are unable to effectively cope with these hazy road images that feature localized light sources or color-shift problems. In response, we present a novel and effective haze removal approach to remedy problems caused by localized light sources and color shifts, which thereby achieves superior restoration results for single hazy images. The performance of the proposed method has been proven through quantitative and qualitative evaluations. Experimental results demonstrate that the proposed haze removal technique can more effectively recover scene radiance while demanding fewer computational costs than traditional state-of-the-art haze removal techniques.