The Single Image Dehazing based onEfficient Transmission Estimation

Contents

Advantages5

Abstract1

Existing System2

Disadvantages3

System Requirements 6

Proposed System4

System Architecture7

Literature Survey8

Abstract

We propose a novel haze imaging model for single image

haze removal. Haze imaging model is formulated using dark channel prior

(DCP), scene radiance, intensity, atmospheric light and transmission medium.

The dark channel prior is based on the statistics of outdoor haze-free images.

We find that, in most of the local regions which do not cover the sky, some

pixels (called dark pixels) very often have very low intensity in at least one

color (RGB) channel. In hazy images, the intensity of these dark pixels in that

channel is mainly contributed by the air light. Therefore, these dark pixels can

directly provide an accurate estimation of the haze transmission. Combining a

haze imaging model and a interpolation method, we can recover a high-quality

haze free image and produce a good depth map.

Existing System

• Many methods have been proposed by using multiple images or

additional information.

• Polarization based methods remove the haze effect through two or

more images taken with different degrees of polarization.

• More constraints are obtained from multiple images of the same scene

under different weather conditions.

• Depth-based methods require some depth information from user inputs

or known 3D models.

Disadvantages

• Need of multiple images for haze removal.

• Computational complexity while considering multiple images at a time.

• Execution time was large

Proposed System

• We propose a new haze removal technique for a single input hazy image

using prior haze imaging model.

• First we have to model the haze image using dark channel prior (DCP),

scene radiance, intensity, atmospheric light and transmission medium.

• Compute the dark channel prior (DCP) with the help of color components

such as R, G, B.

• Estimate the transmission from the normalized haze equation.

• Scene radiance will be recovered by the substitution of the mentioned

parameters in haze imaging model.

• The measure CNR (Contrast to Noise Ratio) will be used to qualify the

performance.

System Requirements

Hardware Specification

– Pentium IV – 2.7 GHz

– 1GB DDR RAM

– 250Gb Hard Disk

Software Specification

– Operating system : Windows 7

– Language : Matlab

– Version : 7.9

Future Enhancement

• In the transmission estimation instead log function we employ a column-wise neighborhood operation with minimum value of modified min channel, for smooth transmission.

• We apply hybrid median filter to the dehazed image to get a better enhanced image.

/AvvenireTechnologies /avveniretech