survey on haze removal techniques

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Survey on Haze Removal Techniques

Lipakshee Bisen, Prof. Mr. Amit Dravid

G.H.Raisoni institute of engineering and technology(GHRIET),Pune

ABSTRACT : This paper analyzed different haze removal methods. Haze causes trouble to

many computer graphics/vision applications as it reduces the visibility of the scene. Air light and

attenuation are two basic phenomena of haze. air light enhances the whiteness in scene and on

the other hand attenuation reduces the contrast. the colour and contrast of the scene is recovered

by haze removal techniques. many applications like object detection , surveillance, consumer

electronics etc. apply haze removal techniques. this paper widely focuses on the methods of

effectively eliminating haze from digital images. it also indicates the demerits of current

techniques. Keywords: Image Dehazing, ICA, Depth, DCP, Contrast enhancement, Polarizers

I. INTRODUCTION

The bad weather conditions may demean the quality of the images of outdoor scenes. It is an

annoying problem for a photographer who captures images but the images results into change of

colours, blur image, etc. This is an ultimatum to reliability of many applications. The unwanted

condition is caused by the atmospheric conditions like haze[1] and fog, which blurs the captured

scene. Always the air is misted by some added particles which are scattered around, and hence,

the reflected light is also scattered which results in less visibility of distant objects. The scattering

is caused by two basic events namely attenuation and airlight [2, 1]. This occurrence affects the

normal work of automatic monitoring system, outdoor recognition system, tracking &

segmentation and intelligent transportation system. In the last few years, a technique has gained popularity and this is known as restoration of

images that are taken into bad atmospheric conditions. This specific task has become important

for several outdoor applications such as remote sensing, intelligent vehicles, object recognition

and surveillance. The processing of recorded bands of reflected light is done in order to restore

the outputs in remote sensing systems. Generally, haze may enervate the light reflected from the

International Journal of Modern Trends in Engineering and Research (IJMTER) Volume 01, Issue 05, [November - 2014] e-ISSN: 2349-9745, p-ISSN: 2393-8161


scenes and in fact merge some additional light in the environment. This effect of haze can be

reduced by haze removal technique by improving the reflected light and avoiding the merging of

additional light in the atmosphere. There are several haze removal techniques such as

polarization[3,4] , independent component analysis, dark channel prior etc.

II. THEORETICAL RELEVANCE

Haze removal techniques are gaining popularity due to its availability in many classifications.

These methods can be used to construct a high quality, noise free, dehaze images. The

classifications are done in two major types image segmentation and image restoration. Due to the

presence of fog, mist, haze into the atmosphere the images captured of outdoor scenes may have

a low quality. In many surveillance and transportation area haze removation is important task.

This approach includes the analysis of scene, extraction of useful information and then detecting

the image. Mostly in a bad weather condition the light that is visible is captivated and is scattered

by other particles or raindrops. This prototype is engaged in many haze removal approaches and

is exhibited as,

I(x) = J(x) t(x) + A (1 t(x)) ----------------------------- (1) Where, I is the haze image on the three R, G, B color channels. J is the scene without haze, t is

the transmission coefficient to describe the percentage of light that can penetrate through haze,

and A is the atmospheric light. Using this atmospheric scattering model to recover the scene J,

the main challenge of haze removal is to estimate the atmospheric light A and the transmission t

from the source image I properly. The dark channel prior is based on the following observation.

On haze-free outdoor images in which most of the non-sky patches contain at least one color

channel has very low intensity at some pixels. By using this it requires some extensive and

complex computations, such as huge matrix multiplication or division, sort, exponent, and

floating point operations. We further investigate some various haze removal methods like

multiple image scheme, single image with depth image scheme and single image scheme. A. Haze Removal methods Haze removal methods can be used to construct a high quality, noise free, dehaze images. The

classifications are done in two major types image segmentation and image restoration. 1) Image Segmentation: As the name suggests, image segmentation is the process of segregation of a digital image into

multiple segments. The purpose of segmentation is to clarify and/or change the representation of

an image into something that is more meaningful and easier to analyze. This technique is

primarily used to locate objects and boundaries in images. Actually image



segmentation is the process of assigning a label to every pixel in an image such that pixels with

the same label share certain visual characteristics. 2) Image Restoration: Image restoration is the process of taking a corrupted/noisy image and evaluating the clean

original image. The image corruption is caused by many reasons such as motion blurs, noise,

camera miss-focus image, etc. The process of image restoration is very different from the

concept of image enhancement. In the image enhancement process, the designing of the image is

done to highlight the feature of the captures image resulting the image more pleasing to the

observer. From a scientific point of view there is no necessity to produce realistic data. No

previous methods are used in image enhancement techniques that are provided by Imaging

packages. In fact with this approach, noise can be removed effectively by relinquishing some

image resolution. But this phenomenon is not always accepted by many applications. As it is in

Fluorescence Microscpe seen the resolution in the z-direction is not good. But the image

restoration techniques recover the haze image with better quality and brightness. For recovering

the object, there must be more advanced image processing techniques available. Increasing

resolution especially in the axial direction removes noise and increasing contrast. B. Haze Removal using dark channel prior :- A remarkable progress in single image haze removal technique is observed in recent days. The

use of stronger assumptions or prior methods may lead to the success of haze removal technique.

Different researchers can use different methods to remove haze from the images. In [5], the

author has used a soft matting algorithm to remove the haze. But this model is physically invalid

and the assumption of constant air light may be unsuitable when the sunlight is very influential.

Tarel uses image restoration technique to recover the haze. The author in [6], estimates the

albedo of the scene and the medium transmission under the assumption that the transmission and

the surface shading are locally uncorrelated. This technique is physically possible and can give

imposing results. But there are some drawbacks of this system, as it cannot dark hazy images and

it may also fail when the assumption is broken.

III. DEHAZING METHODS Haze removal techniques can be classified into two categories which are as follows : 1) multiple

image dehazing method 2) single image dehazing methods



2.1. Multiple Image Dehazing Methods This method prefers two or more images or multiple images of same scene. It completely avoids

unknown and attains known methods only. Explanation of the methods under this category is

given below: 2.1.1 Weather condition based method This techniques utilizes multiple images(7,2,8) adapted from various weather circumstances. In

the basic method the variations of two or more images of same scene are considered. These

images possess distinct characteristics of the contributing medium on the one hand it enhances

visibility but on the other hand it also make the user wait till the characteristics of the medium

changes. This techniques does not immediately deliver the results. this methods is also unable to

handle dynamic scenes.

(a) Hazy Image (c) Dehazed Image

(b) Hazy Image (d) Clear Weather Image

Figure 1. Multiple Image dehazing



Haze Removal

Techniques

Multiple Image Haze Single Image Haze

removal technique removal technique

Weather condition Contrast maximization

based technique technique

Polarization based Independent

technique component analysis

Depth map based

technique Dark channel prior

technique

Antistrophic Diffusion

technique

Figure 2. Classification of Haze removal methods 2.1.2 Polarization based method This methods having different polarization filters(9,10) but of the same scene are considered.

First of all, in this method distinct images are captured by ratting a polarizing filter. but the

treatment results of dynamic scene is not really good. The demerits of this method are- It require special equipment like polarizers.

It is not applicable to dynamic scene where changes are more quick than filter rotation.

It does not furnish better results.



(a) Best Polarization State (b)Worst Polarization State Figure 3. Image dehazing

using polarizing filters 2.1.3 Depth map based method This method depth information for haze removal is considered. here we consider 3D geometrical

model(2, 7, 10) of scene is given by certain databases like google maps and also considers the

texture of the scene is supplied (from aerial photos or satellite pictures). This 3D model aligns

hazy image and provides the scene depth[11]. This method wants interaction to align 3D model

[12] with the scene and also provide accurate results. In this method special equipments are not

needed. The demerits of this method are:

This method require user interaction

This method is not automatic

This method needs an estimation of more parameters, and the extra information not easy

to adopt.

(a) Hazy image (b) 3D structural model (c) Dehazed Image

Figure 4. Depth map based method



2.2 Single image dehazing method Unlike previous method this method only want a single input image(1,13). This method depends

upon statistical assumption [14] and essence of the scene and it also reclaim the scene data based on

last data from single image. This method is now attracting many researchers. Following are the

methods which come under this category. 2.2.1 Contrast maximization method Haze reduces the contrast elimination of the haze increase the contrast of the image. This method

increases the contrast under the constraint. As this method does not physically enhance depth or

brightness, the resultant image have greater saturation values. The results also constitute halo effects

at depth discontinuities.

a) Hazy Image (b) Restored Image

Figure 5. Contrast Maximization Method

2.2.2 Independent Component Analysis(ICA) ICA is a statistical method of dividing two additional components from a single. this method is used

by fatal [13] and it is based on the assumption that surface shading are statistically uncorrelated in

local patch. this approach provides good results and physically valid , but one of the most important

disadvantage of this method is that it does not give paper result in case of dense haze.



(a) Hazy Image (b) Haze-free image

Figure 6. Independent component analysis

2.2.3 Dark Channel prior The dark channel prior [14] is based on the statistics of outdoor haze-free images. In most of the non-

sky patches, at least one color channel (RGB) has very low intensity at some pixels (called dark

pixels). These dark pixels provide the estimation of haze transmission. This approach is physically

valid and work well in dense haze. When the scene objects are similar to the air light then it is

invalid.

(a)Hazy Image (b) Recovered Depth map (c) Haze-free image

Figure 7. Dark channel prior



2.2.4 Anisotropic diffusion Anisotropic diffusion [15] is a technique that reduces haze without removing image parts such as

edges, lines or other details that are essential for the understanding of the image. Its flexibility permits

to combine smoothing properties with image enhancement qualities. Tripathi [16] present an

algorithm uses anisotropic diffusion for refining air light map from dark channel prior. Antistrophic

diffusion is used to smooth the airlight map. It performs well in case of heavy fog.

IV. RELATED WORK

The author Schechner and et al in his paper has given his work, which is based on the fact that the

scattered airlight is partially polarized. This airlight is scattered by the atmospheric particles. But

only the polarization filtering cannot remove the haze effect. In the proposed work, the image

formation process is shown where the image is a clean image. The polarization effect is considered

and the inverting process is utilized, where it outputs into a haze free image. Two components are

used to compose the image, one is known as scene radiance and the other is airlight. Scene radiance

is in the absence of haze. And airlight is the ambient light that is scattered towards the viewer. For

recovering the two components, there is a need for two non-dependent images. And these images can

easily be acquired because airlight is partially polarized. This approach can be immediately applied.

It does not require the change in weather conditions. The images that are taken by a polarizer uses the

concept of polarization filtering. This polarization filtering is used in photography across haze. The

aim of polarization filtering is to improve the contrast of the input image.

In [13] Fattal proposed a new approach for single image dehazing which try to implement haze free

image from the hazy image. Fattal formulated the refined image formation model that relates to the

surface shading and the transmission function.

He and et al [14] dark channel prior is based on prior assumption. It has been observed that in most of

the local regions which do not cover the sky, some pixels have very low intensity in at least one color

(RGB) channel and these pixels are known as the dark pixels. In hazy images the intensity of the dark

pixels in that color channel is basically contributed by the airlight and these dark pixels are used to

estimate the haze transmission. After estimation of the transmission map for each pixel, combining

with the haze imaging model and soft matting technique [17] to recover a high quality haze free

image.

Ancuti and et al. [18] is described haze is atmospheric term which degrades the outdoor image

visibility under the bad weather condition. This paper describes single image dehazing approach



which is based on fusion strategy and it has been derived from the original hazy image inputs by

applying a white balance and contrast enhancing procedure. The fusion enhancement technique

estimates perceptual based qualities known as the weight maps for each pixel in the image. These

weight maps control the contribution of each input to the final obtained result. Different weight maps

like luminance, chromaticity and saliency are computed and to minimize the artifacts produced

during the weight maps, the multiscale approach uses the laplacian pyramid representations

combination with gaussian pyramids of normalized weights. As this approach tries to minimize the

artifacts per pixel based has a greater improvement rather than considering a patch based method due

to the assumption of contrast airlight in the patch.

Xie and et al [19] paper describes the dehazing process using dark channel prior and multi-scale

retinex. This paper also focuses on the approach which provides the automatic and fast acquisition of

transmission map of the scene. The proposed approach is based on the implementing the multi scale

retinex algorithm on the luminance component in YCbCr space

of the input image to get the pseudo transmission map. The obtained pseudo transmission map is very

much similar to the transmission map obtained by using the dark channel prior by He et.al[14].

Combining the haze imaging model and the dark channel prior, a high quality haze free image is

recovered.The input hazy image has been transformed from RGB color space to YCbCr space and

then by using the multiscale retinex algorithm, on the luminance component of the transformed

image with some adjustment to get the transmission map. Then combining both the haze image

model and the retinex algorithm a better haze free image is recovered.

Schaul and et al. [20] focused on the fact that in outdoor photography, the distant object are appeared

as blurred and loses its color and visibility due to the degradation level affected by the atmospheric

haze. In this paper the key idea is used to fusion of the visible and a near-infrared image of the given

input image to obtain a dehazed image and it also describes the multiresolution approach using the

edge preserving filter to minimize the artifacts those are produced during the dehazing process.

IV CONCLUSION

Many vision applications apply haze removal algorithms. In past few days it was discovered that

researchers have neglected many problems like no technique is appropriate for distinct circumstances.

We have came to the conclusion that the presented methods have ignored the techniques to diminish

the noise problem which is given in the output images of the current fog removal algorithms. The issue

of lack of uniformity and over illumination is also an problem for



dehazing the methods. so it is essential to rectify the current techniques in such a manner that rectified method will work efficiently.

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