An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by

Intelligent Transportation Systems

Abstract

• The visibility of images of outdoor road scenes will generally become degraded when captured during inclement weather conditions.

• 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.

Introduction

• Visibility in road images can be degraded due to natural atmospheric phenomena such as haze, fog, and sandstorms.

• This visibility degradation is due to the absorption and scattering of light by atmospheric particles.

• 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.

Haze removal methodsExample images

(a),(b) Foggy images (c) Dehazed image, (d) Clear weather images

Multiple Image Approaches

One Image + Depth + Texture

• Assumes textures of the scene are

• Given (From Satellite Or Aerial Photos)

• Requires User Interaction To Align The 3D Model With The Scene

• Very Accurate Results

EXISTING SYSTEMS

• Optical Model• Haze Removal Using Dark Channel Prior

Optical Model

• In computer vision and pattern analysis, the optical model is widely used to describe the digital camera information of a hazy image under realistic atmospheric conditions in the RGB color space as

Fig.. Pictorial description of hazy image acquisition via the optical model.

Haze Removal Using Dark Channel Prior

• The dark channel prior is a state-of the- art image restoration technique by which to remove haze from a single image. In order to estimate the amount of haze in an image, dark channel J dark can be expressed as

Statistics of the dark channel

( )darkJ x

Except for the sky region, the intensity of is low and tends to be zero

Apply dark channel prior

Haze-free image The dark channel of haze-free image

Disadvantages of Existing systems

• Low image quality.• It not estimate the position of the atmospheric

light .• In these not used in 3D geometric model .

Proposed system

• In this we propose an efficient visibility enhancement algorithm Road Scenes Captured

Block diagram of proposed system

HDCP Module

• Hybrid dark channel prior (HDCP)• HDCP module can produce a restored image

that is not underexposed by using a procedure based on the dark channel prior technique.

• Equation can be rewritten via the HDCP as

Frame work of HDCP

Estimating the Transmission Map

• In a single hazy image, these dark channel values can provide a direct and accurate estimation of haze transmission and atmospheric light Ac in the RGB color space as

Cont…HDCP is transmission map can be expressed as

To estimate of atmospheric light

Pick the top 0.1% brightest pixels in the dark channel

Color Analysis Module

• The particles of sand in the atmosphere caused by sandstorms absorb specific portions of the color spectrum.

• This phenomenon leads to color shifts in images captured during such conditions, resulting in different color channel distributions.

• Each RGB color channel for a typical image, which is described as

• Each RGB color channel in order to avoid color shifts in the restored image. This can be measured as

Visibility Recovery module

• In order to produce a high-quality haze-free image captured in different environments, we combine the information provided via the HDCP and CA modules to effectively recover the scene radiance.

Advantages• Accurately estimate the position of atmospheric light.• The use of the proposed method is quantitative and

qualitative evaluation of intelligent transportation systems

• It improves visibility in hazy images.• It accurately estimates the position of atmospheric light.• The proposed Technique is produced satisfactory

restore image

Applications

• Improvement of traffic surveillance application

• Vehicle classification, vehicle recognition, Vehicle Tracking, Collision avoidance

• Automated motion detection