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.

1. The Single Image Dehazing based on
Efficient Transmission Estimation

2. Contents
1
Abstract
2
Existing System
3
Disadvantages
4
Proposed System
5
Advantages
6
System Requirements
7
System Architecture
8
Literature Survey

3. 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.

4. 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.

5. Disadvantages
•
Need of multiple images for haze removal.
•
Computational complexity while considering multiple
images at a time.
•
Execution time was large

6. 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.

7. 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

8. 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.

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