Haze Removal using Matlab

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Haze Removal using Matlab

Image enhancement technology is one of the basic technologies in the image processing field. The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers or to provide `better’ input for other automated image processing techniques. Image Segmentation is one of the vital steps of satellite Image processing for gathering information from satellite images. To study the effectiveness of noise in satellite images, different types of noise like Gaussian, Poisson, salt & pepper, and speckle noise are added to the original image. The discrete wavelet transform (DWT) and Bayes Shrink soft thresholding have then applied the removal of noisy pixels and smoothened the image. The proposed technique, computationally more efficient than the spatial domain-based method, is found to provide better enhancement compared to other compressed domain-based approaches. In the final stage, the fuzzy-based modified FCM clustering is performed on the denoised images to produce clusters of segmented results.Haze Removal using Matlab

Haze Removal using Matlab


Haze-removal algorithms are used to obtain clean, haze-free images with enhanced saturation and contrast. Haze or fog is caused by microscopic aerosols distributed in the air. Cameras and the human eye lack the sensitivity to make out these aerosols; however, airborne particles can affect radiance in other ways, such as Rayleigh scattering, Mie scattering, and the Tyndall effect. Researchers have observed that these effects obey Koschmieder’s Law in proposing a model for the removal of haze from video clips obtained using a fixed camera position, wherein some of the unknown variables can be eliminated using a clip containing time series data. In contrast, single image de-hazing methods focus more on image enhancement than a restoration based on strict physical laws. Most of these methods are based on augmenting the attenuated signal with a priori knowledge related to dynamic range, saturation, or contrast. Based on the observation that atmospheric light reduces contrast, Tan? sought to restore images by maximizing in-patch contrast defined as the sum of the gradient using an energy maximization framework. For removing these types of noises inside the image we are going to use such techniques which are mentioned in the abstract of this project these techniques provide a very good clear output image without haze which is simply called a dazed image. Haze Removal using Matlab

Haze Removal using Matlab

Existing Method:

  • Based Gaussian Filter Kernel function they have done enhancement for image
  • CWT-Based Image Resolution Enhancement
  • Threshold Segmentation

The drawback of the Existing Technique:

  • Loses in edges in the final sharper image.
  • The edges of the color image could not be handled well.
  • Gaussian Filter leads to the inaccurate background Image
  • The edges of the color image could not be handled well

Proposed Method:

  • DWT-based resolution enhancement
  • Bayes Shrink soft Thresholding
  • Fuzzy C-Means Clustering

Advantages of Proposed Method:

  • Superior Resolution, when compared to existing techniques.
  • Greater performance Ratio.

Haze Removal using Matlab

Block diagram:

Haze Removal using Matlab

Requirement Specifications:

Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk


Haze Removal using Matlab


  • [1]Efficient image dehazing with boundary constraint and contextual regularization, G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, 2013
  • [2] Improved visibility of road scene images under heterogeneous fog, J.-P. Tarel, N. Hauti?re, A. Cord, D. Gruyer, and H. Halmaoui, 2010
  • [3]Chromatic framework for vision in bad weather, S. G. Narasimhan and S. K. Nayar,2000
  • [4]Single image haze removal using dark channel prior, K. He, J. Sun, and X. Tang, 2011
  • [5]Bayesian defogging, K. Nishino, L. Kratz, and S.2012
  • [6] M. Almeida and L. Almeida, ?Blind and semi-blind deblurring of natural images,? IEEE Transactions on Image Processing, vol. 19, no. 1, pp. 36?52, Aug. 2010.
  • [7] Q. Shan, J. Jia, and A. Agarwala,?High-quality motion deblurring from a single image,? ACM Trans. Graph., vol. 27, no. 3, pp. 721? 730, Aug. 2008.
  • [8] N. Joshi, R. Szeliski, and D. Kriegman, ?Post estimation using sharp edge prediction,? in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Anchorage, AL, USA, 2008.
  • [9] A. Levin, Y. Weiss, F. Durand, and W. Freeman,?Ef? client marginal likelihood optimization in blind deconvolution,?inProc.IEEE conf.ComputerVisionandPattern Recognition, Colorado Springs, CO, USA, 2011.
  • [10] D. Krishnan, T. Tay, and R. Fergus,?Blind deconvolution using a normalized sparsity measure,? in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 2011.


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