The document summarizes a research project on single image haze removal using a variable fog-weight. It begins with an introduction on how haze degrades image quality and the need for haze removal techniques. It then discusses the motivation, literature review, objective, and main contribution of the proposed method. The method uses the dark channel prior to estimate the transmission map and atmospheric light. It then applies a variable fog-weight to modify the transmission map and reduce halo artifacts. A guided filter is used for transmission refinement before recovering the haze-free scene radiance. The method aims to improve on existing techniques by reducing time complexity and halo artifacts while enhancing image visibility.

1. Department of Electronics and
Communication Engineering
Single image haze removal using variable
fog-weight
Presented by:
Name : MD Mohsin Ghazi
Roll No. : 1609731055
B.Tech (ECE)

2. Contents
Introduction
Motivation
Literature Review
Objective
Main Contribution
Haze removal method
Results and comparison
Conclusion
List of Publications
References

3. Introduction
• The quality of image is generally degraded due to bad weather condition and presence of
suspended particle like fog, dust, mist and haze etc. in the atmosphere.
• Therefore, the dehazing of the image is needed to overcome the impact this unwanted weather
factors.
• Dehazing is the procedure to extract the haze effect from the degraded image and reconstruct
the original colours of the degraded image.
• Reconstruction of original colours of the degraded image captured under bad weather condition
is highly desired in both computational photography as well as computer vision applications.

4. Introduction (continued)
• Therefore, extraction of haze from the captured image is most challenging task.
• To enhance the visibility and make image usable, many of the researchers had made
numerous efforts and proposed different haze removal techniques.
• The role of haze removal is to remove the impact of weather factor and improve the visibility of
the image.
• Figure Shows the Image degraded by haze with respect to dehazed image.

5. Motivation
• All Conventional vision system are designed to perform in clear weather.
• Under adverse weather conditions such as “Mist, Fog, Rain, and Snow” the contrast and
color of images are drastically altered or degraded.
• Most outdoor vision applications such as “Autonomous Navigation, real-time Surveillance,
Remote Sensing, and Automatic Target Recognition (ATR)” are incomplete without
mechanism that guarantee satisfactory performance under poor weather conditions.
• It is imperative to remove the weather effects from images in order to make Vision Systems
more reliable.

6. Literature review
S. No. References Findings Limitations
1. Narasimhan, S.G. and Nayar, S.K., 2003. Contrast
restoration of weather degraded images. IEEE
transactions on pattern analysis and machine
intelligence, 25(6), pp.713-724.
Restoration Based
Method, Algorithm is
based on multiple
number of images.
Scene information is
needed from the sensors
or an existing database.
2. Narasimhan, S.G. and Nayar, S.K., 2000, June.
Chromatic framework for vision in bad weather. In
Proceedings IEEE Conference on Computer Vision
and Pattern Recognition. CVPR 2000 (Cat. No.
PR00662) (Vol. 1, pp. 598-605). IEEE.
General Chromatic
Framework Model,
Algorithm is based
on multiple number
of images.
Scene information is
needed from the sensors
or an existing database.

7. Literature Review (continued)
S. No. References Findings Limitations
3. Treibitz, T. and Schechner, Y.Y., 2009, June.
Polarization: Beneficial for visibility
enhancement?. In 2009 IEEE Conference on
Computer Vision and Pattern Recognition (pp.
525-532). IEEE.
Polarization Based
Method
It is complicated to obtain
source image.
4. Schechner, Y.Y., Narasimhan, S.G. and Nayar,
S.K., 2003. Polarization-based vision through
haze. Applied optics, 42(3), pp.511-525.
Different Polarizing
Condition
Scene information is
needed from the sensors
or an existing database.

8. Literature Review (continued)
S. No. References Findings Limitations
5. Shwartz, S., Namer, E. and Schechner, Y.Y.,
2006, June. Blind haze separation. In 2006
IEEE Computer Society Conference on
Computer Vision and Pattern Recognition
(CVPR'06) (Vol. 2, pp. 1984-1991). IEEE.
Different Polarizing
Condition, Independent
Component Analysis
It is complicated to obtain
source image. Method is
incompatible, Complex
and Time Consuming.
6. Schechner, Y.Y. and Averbuch, Y., 2007.
Regularized image recovery in scattering
media. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 29(9), pp.1655-1660.
Polarization Based
Method.
It is complicated to obtain
source image.

9. Literature Review (continued)
S. No. References Findings Limitations
7. Kopf, J., Neubert, B., Chen, B., Cohen, M.,
Cohen-Or, D., Deussen, O., Uyttendaele, M.
and Lischinski, D., 2008. Deep photo: Model-
based photograph enhancement and viewing.
ACM transactions on graphics (TOG), 27(5),
pp.1-10.
Additional information
based
Lengthy Process, Time
Complexity and not
effective output.
8. Kim, J.H., Sim, J.Y. and Kim, C.S., 2011, May.
Single image dehazing based on contrast
enhancement. In 2011 IEEE International
Conference on Acoustics, Speech and Signal
Processing (ICASSP) (pp. 1273-1276). IEEE.
Local Contrast
Enhancement.
Output image become
over saturated and looks
like unnatural.

10. Literature Review (continued)
S. No. References Findings Limitations
9. Tan, R.T., 2008, June. Visibility in bad weather
from a single image. In 2008 IEEE Conference
on Computer Vision and Pattern Recognition
(pp. 1-8). IEEE.
Local Contrast
Enhancement.
Output image become
over saturated and looks
like unnatural.
10. Schaul, L., Fredembach, C. and Süsstrunk, S.,
2009, November. Color image dehazing using
the near-infrared. In 2009 16th IEEE
International Conference on Image Processing
(ICIP) (pp. 1629-1632). IEEE.
Multi-solution Image
Fusion, Near Infrared.
It is complicated to obtain
source image and yield
few halo artifacts.

11. Literature Review (continued)
S. No. References Findings Limitations
11. Tarel, J.P. and Hautiere, N., 2009, September.
Fast visibility restoration from a single color or
gray level image. In 2009 IEEE 12th
International Conference on Computer Vision
(pp. 2201-2208). IEEE.
Dedicated Tone
Mapping, White
Balance
Output image is over
saturated and halo
artifacts are also
appeared at the boundary
of the edge.
12. Fattal, R., 2008, Single image dehazing. ACM
SIGGRAPH 2008 Papers on - SIGGRAPH ’08.
Constant Albedo
Image, Multi Albedo
Image.
Output image is over
saturated and looks like
unnatural.

12. Literature Review (continued)
S. No. References Findings Limitations
13. He, K., Sun, J. and Tang, X., 2010. Single
image haze removal using dark channel
prior. IEEE transactions on pattern analysis
and machine intelligence, 33(12), pp.2341-
2353.
Dark Channel Prior,
Soft Matting
Due to the use of soft matting
algorithm takes a lot of time to
execute and in output image,
few halo artifacts are also
appeared.
14. Huang, S.C., Chen, B.H. and Wang, W.J.,
2014. Visibility restoration of single hazy
images captured in real-world weather
conditions. IEEE Transactions on Circuits
and Systems for Video Technology, 24(10),
pp.1814-1824.
Depth Estimation
Module, Colour
Analysis Module,
Visibly Restoration
Module.
Does not remove haze properly
in case of heavy hazy input
image.

13. Literature Review (continued)
S. No. References Findings Limitations
15. Wang, J.B., He, N., Zhang, L.L. and Lu, K.,
2015. Single image dehazing with a physical
model and dark channel prior.
Neurocomputing, 149, pp.718-728.
Dark Channel Prior,
Variogram
Failed in case of heavy
hazy input image.
16. Gao, R., Fan, X., Zhang, J. and Luo, Z., 2012,
September. Haze filtering with aerial
perspective. In 2012 19th IEEE International
Conference on Image Processing (pp. 989-
992). IEEE.
Dark Channel Prior Failed in sky region and
heavy hazy input image.

14. Literature Review (continued)
S. No. References Findings Limitations
17. Kim, K., Kim, S. and Kim, K.S., 2017. Effective
image enhancement techniques for fog-affected
indoor and outdoor images. IET Image
Processing, 12(4), pp.465-471.
Dark Channel Prior,
Contrast Limited
Adaptive Histogram
Equalization with
Discrete Wavelet
Transform.
Inaccurate estimation of
transmission map in DCP
and highly affected by
halo artifact.
18. Wang, Z., Hou, G., Pan, Z. and Wang, G.,
2017. Single image dehazing and denoising
combining dark channel prior and variational
models. IET Computer Vision, 12(4), pp.393-
402.
Layered Total Variation,
Multichannel Total
Variation, Colour Total
Variation, Dark Channel
Prior.
Failed in case of heavy
hazy input image and
also highly affected by
halo effect at the
boundary of the edge.

15. Literature Review (continued)
S. No. References Findings Limitations
19. Xu, L., Wei, Y., Hong, B. and Yin, W.,
2019. A Dehazing Algorithm Based on
Local Adaptive Template for Transmission
Estimation and Refinement. IEEE Access,
7, pp.125000-125010.
Dark Channel
Prior, Local
Adaptive
Template
Output become over saturated and
looks like unnatural image. this
algorithm is failed in case of heavy
haze and change only colour of the
hazy part.
20. Kim, S.E., Park, T.H. and Eom, I.K., 2019.
Fast single image dehazing using
saturation based transmission map
estimation. IEEE Transactions on Image
Processing, 29, pp.1985-1998.
Dark Channel
Prior, Removing
Colour Veil.
Not effective in case of heavy hazy
input image and output image is still
hazy.

16. Literature Review (continued)
S. No. References Findings Limitations
21. He, K., Sun, J. and Tang, X., 2012. Guided
image filtering. IEEE transactions on pattern
analysis and machine intelligence, 35(6),
pp.1397-1409.
Guided-Filter Halo effect highly
appeared at the boundary
of the edge.
22. Wang, W., Chang, F., Ji, T. and Wu, X., 2018. A
fast single-image dehazing method based on a
physical model and gray projection. IEEE
Access, 6, pp.5641-5653.
Dark Channel Prior Failed to remove haze
properly in output image
and halo artifacts are
present at the boundary of
the edge.

17. Literature Review (continued)
S. No. References Findings Limitations
23. Shen, L., Zhao, Y., Peng, Q., Chan, J.C.W. and
Kong, S.G., 2018. An iterative image dehazing
method with polarization. IEEE Transactions on
Multimedia, 21(5), pp.1093-1107.
Dark Channel Prior,
Polarization
Failed in case of heavy
haze present in the input
image.
24. Salazar-Colores, S., Cabal-Yepez, E., Ramos-
Arreguin, J.M., Botella, G., Ledesma-Carrillo,
L.M. and Ledesma, S., 2018. A fast image
dehazing algorithm using morphological
reconstruction. IEEE Transactions on Image
Processing, 28(5), pp.2357-2366.
Dark Channel Prior,
Morphological
Reconstruction
Failed to remove haze
properly in output image,
especially the areas
where depth is rapidly
changing in the image.

18. Literature Review (continued)
S. No. References Findings Limitations
25. Kang, C. and Kim, G., 2018. Single image haze
removal method using conditional random
fields. IEEE Signal Processing Letters, 25(6),
pp.818-822.
Conditional Random
Field, Tree-reweighted
Failed to remove haze
properly from the input
image if haze density is
high in input image.
26. Liu, F. and Yang, C., 2014, August. A fast
method for single image dehazing using dark
channel prior. In 2014 IEEE International
Conference on Signal Processing,
Communications and Computing (ICSPCC)
(pp. 483-486). IEEE.
Dark Channel Prior Output image is affected
by block effect and also
failed to remove haze
properly where haze
density is high in input
image.

19. Objective
• In this proposed work, the main focus is to reduce the time complexity, minimizing the
halo artifact and improve the visibility of the input image.

20. Main contribution
• The main contribution of the proposed framework is to vary the fog weight and to modify the
inaccurate transmission map in DCP depending on the haze density of the input hazy image.
• After modifying the transmission map, a guided filter is used to avoid the halo artifact up to
the threshold which results a high-quality haze free image.

21. Haze removal method
Input image
Dark channel
prior
Atmospheric
light
Modified transmission
map
Guided filter
Scene radiance
recovery
Haze free image
Flow chart of the proposed method

22. Haze removal method (continued)
Atmospheric scattering model:
• To describe the formation of hazy image, the atmospheric scattering model is widely used in
computer vision and image processing [22].
𝑯 𝒙 = 𝑺 𝒙 𝑻 𝒙 + 𝑨 𝟏 − 𝑻 𝒙
Hazy image Recovered image Transmission map
Atmospheric lightAtmospheric light

23. Haze removal method (continued)
Dark channel prior (DCP):
• DCP is focused on statistical measurements of various out-of-door haze-free images. It has
been found that haze-free images consist of a few pixels that have very small intensities in at
least one color channel in the most of the non-sky areas [13].
𝑺 𝒅𝒂𝒓𝒌 𝒙 = 𝐦𝐢𝐧
𝒄∈ 𝒓,𝒈,𝒃
𝐦𝐢𝐧
𝒚∈𝛀 𝒙
𝑺 𝒄 𝒚
Estimate atmospheric light:
• The pixels of the maximum intensity have been regarded as atmospheric light in [9] and is
further refined in [12].
• In the proposed method, the top 0.1% brightest pixels in the dark channel are picked. In these
pixels, the highest intensity pixels are elected as atmospheric light.

24. Haze removal method (continued)
Modified transmission map:
• The transmission map T is obtained by applying minimal operation on the dark channel prior.
• The modified transmission map using DCP statistic is presented by equation:
𝑻 𝒙 = 𝟏 − 𝝎 𝒗𝒓. 𝐦𝐢𝐧
𝒄
𝐦𝐢𝐧
𝒚∈𝛀 𝒙
𝑯 𝒄 𝒚
𝑨 𝒄
𝝎 𝒗𝒓. = 𝐥𝐨𝐠 𝟏𝟎 𝑹 4≤ 𝑹 ≤ 𝟏𝟎
𝝎 𝒗𝒓. = Variable fog-weight
𝛀(𝒙) = Local patch in dark channel prior
R = Real Number

25. Haze removal method (continued)
Guided Filter:
• Transmission refiner Guided Filter is used to avoid the halo artifact at the boundary
of the object into the image and produce high-quality haze free image.
• Considering the guidance image is H, p is image to be filtered and q is resulting
image. The local linear model of the Guided Filter is given as:
𝒒𝒊 = 𝒂 𝒌 𝑯𝒊 + 𝒃 𝒌 , 𝒊 ∈ 𝝎 𝒌
• Where 𝝎 𝒌 is a window centred at pixel k, with radius r, and 𝒂 𝒌 , 𝒃 𝒌 are known to be
linear coefficient [17].

26. Haze removal method (continued)
Recovery of scene radiance:
• With the help of modified transmission map and estimated atmospheric light, the scene radiance
S 𝒙 is recovered by using atmospheric scattering model.
S 𝒙 =
𝑯 𝒙 −𝑨
𝐦𝐚𝐱(𝑻 𝒙 ,𝑻 𝟎)
+ 𝑨
• Where 𝑻 𝟎 is Lower Bound of transmission map and its value is restricted to 0.1 in this paper.

27. Results and comparison
Input hazy image Our result without refinement Our result with refinement
• All the input hazy image are taken from the dataset of He et. al. [13] and dataset of Kim et. al. [21].

28. Results and comparison (continued)
Hazy images DCP+GF [17] He et. al. [13] Our results

29. Results and comparison (continued)
Hazy images DCP+GF [17] He et. al. [13] Our results

30. Results and comparison (continued)
Images He et al. DCP+GF Proposed method
Image-1 5.373 0.697 0.388
Image-2 9.888 1.233 0.681
Image-3 15.916 1.898 1.060
Image-4 23.614 2.468 1.486
Image-5 33.653 3.315 2.024
Image-6 42.861 4.733 2.601
Image-7 61.726 5.545 3.299
Image-8 80.144 6.804 4.029
Table 1. Comparison of computation time (unit: second)

31. Conclusion
• This algorithm, mainly focused on avoiding block effect at the boundary of the edge and
modify the transmission map to provide haze free and under saturated image, even when
haze density is low or high in the input image.
• After experiments on different type of hazy image, it is confirmed that the proposed algorithm
can accurately estimate the transmission map and effectively avoid the block effect.
• The experimental results demonstrated that the performance of the proposed algorithm is
best in terms of both computational complexity as well as quality of the image.

32. List of Publications
IOP Science : Journal of Physics Conference Series
1. Mohammed Shoaib, Mohd Mohsin, Imbeshat Khalid Ansari, Harshat Maddhesiya, Upendra Kumar
Acharya, “Single image haze removal using variable fog-weight”, 2020 First International Conference
on Advances in Physical Science and Material (ICAPSM 2020). (Accepted and Presented)

33. References
1. Narasimhan, S.G. and Nayar, S.K., 2003. Contrast restoration of weather degraded images. IEEE
transactions on pattern analysis and machine intelligence, 25(6), pp.713-724.
2. Narasimhan, S.G. and Nayar, S.K., 2000, June. Chromatic framework for vision in bad weather. In
Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.
PR00662) (Vol. 1, pp. 598-605). IEEE.
3. Treibitz, T. and Schechner, Y.Y., 2009, June. Polarization: Beneficial for visibility enhancement?. In 2009
IEEE Conference on Computer Vision and Pattern Recognition (pp. 525-532). IEEE.
4. Schechner, Y.Y., Narasimhan, S.G. and Nayar, S.K., 2003. Polarization-based vision through haze.
Applied optics, 42(3), pp.511-525.
5. Shwartz, S., Namer, E. and Schechner, Y.Y., 2006, June. Blind haze separation. In 2006 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition (CVPR'06) (Vol. 2, pp. 1984-1991).
IEEE.

34. References (continued)
6. Schechner, Y.Y. and Averbuch, Y., 2007. Regularized image recovery in scattering media. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 29(9), pp.1655-1660.
7. Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M. and Lischinski,
D., 2008. Deep photo: Model-based photograph enhancement and viewing. ACM transactions on
graphics (TOG), 27(5), pp.1-10.
8. Kim, J.H., Sim, J.Y. and Kim, C.S., 2011, May. Single image dehazing based on contrast enhancement.
In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp.
1273-1276). IEEE.
9. Tan, R.T., 2008, June. Visibility in bad weather from a single image. In 2008 IEEE Conference on
Computer Vision and Pattern Recognition (pp. 1-8). IEEE.
10.Schaul, L., Fredembach, C. and Süsstrunk, S., 2009, November. Color image dehazing using the near-
infrared. In 2009 16th IEEE International Conference on Image Processing (ICIP) (pp. 1629-1632).
IEEE.

35. References (continued)
11.Tarel, J.P. and Hautiere, N., 2009, September. Fast visibility restoration from a single color or gray level
image. In 2009 IEEE 12th International Conference on Computer Vision (pp. 2201-2208). IEEE.
12.Fattal, R., 2008, Single image dehazing. ACM SIGGRAPH 2008 Papers on - SIGGRAPH ’08.
13.He, K., Sun, J. and Tang, X., 2010. Single image haze removal using dark channel prior. IEEE
transactions on pattern analysis and machine intelligence, 33(12), pp.2341-2353.
14.Huang, S.C., Chen, B.H. and Wang, W.J., 2014. Visibility restoration of single hazy images captured in
real-world weather conditions. IEEE Transactions on Circuits and Systems for Video Technology, 24(10),
pp.1814-1824.
15.Wang, J.B., He, N., Zhang, L.L. and Lu, K., 2015. Single image dehazing with a physical model and
dark channel prior. Neurocomputing, 149, pp.718-728.
16.Gao, R., Fan, X., Zhang, J. and Luo, Z., 2012, September. Haze filtering with aerial perspective. In 2012
19th IEEE International Conference on Image Processing (pp. 989-992). IEEE.

36. References (continued)
17.He, K., Sun, J. and Tang, X., 2012. Guided image filtering. IEEE transactions on pattern analysis and
machine intelligence, 35(6), pp.1397-1409.
18.Kim, K., Kim, S. and Kim, K.S., 2017. Effective image enhancement techniques for fog-affected indoor
and outdoor images. IET Image Processing, 12(4), pp.465-471.
19.Wang, Z., Hou, G., Pan, Z. and Wang, G., 2017. Single image dehazing and denoising combining dark
channel prior and variational models. IET Computer Vision, 12(4), pp.393-402.
20.Xu, L., Wei, Y., Hong, B. and Yin, W., 2019. A Dehazing Algorithm Based on Local Adaptive Template for
Transmission Estimation and Refinement. IEEE Access, 7, pp.125000-125010.
21.Kim, S.E., Park, T.H. and Eom, I.K., 2019. Fast single image dehazing using saturation based
transmission map estimation. IEEE Transactions on Image Processing, 29, pp.1985-1998.
22.McCartney, E.J., 1976. Optics of the atmosphere: scattering by molecules and particles. nyjw.

37. References (continued)
23.Wang, W., Chang, F., Ji, T. and Wu, X., 2018. A fast single-image dehazing method based on a
physical model and gray projection. IEEE Access, 6, pp.5641-5653.
24.Shen, L., Zhao, Y., Peng, Q., Chan, J.C.W. and Kong, S.G., 2018. An iterative image dehazing method
with polarization. IEEE Transactions on Multimedia, 21(5), pp.1093-1107.
25.Salazar-Colores, S., Cabal-Yepez, E., Ramos-Arreguin, J.M., Botella, G., Ledesma-Carrillo, L.M. and
Ledesma, S., 2018. A fast image dehazing algorithm using morphological reconstruction. IEEE
Transactions on Image Processing, 28(5), pp.2357-2366.
26.Kang, C. and Kim, G., 2018. Single image haze removal method using conditional random fields. IEEE
Signal Processing Letters, 25(6), pp.818-822.
27.Liu, F. and Yang, C., 2014, August. A fast method for single image dehazing using dark channel prior. In
2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)
(pp. 483-486). IEEE.

38. THAKN YOU