IV CONTRAST ENHANCEMENTContrast is a measure of how much the

IV. CONTRAST ENHANCEMENT

Contrast is a measure of how much the pixel brightness changes relative to the average brightness. A technique called bar chart stretching is accustomed shift the element values to fill the whole brightness vary, leading to high distinction. This is often achieved by two operation stretching and gamma correction 1) the primary step is to seek out the element values that ought to get mapped to 1/3 and 100% brightness. Ant real world image has noise, however to stay the noise from unduly influencing and stretching, associate in nursing assumption is made: a little share of the brightness and darkest pixels’ area unit unheeded, writing them of two sensing element noise.

The stretching operation sets the intensity values that represents all time low 1 chronicles (0.01) and also the prime 1 chronicles (0.99) of the vary because the adjustment limits. By trimming the extremes at each ends of the intensity vary, one will build additional space within the adjust dynamic vary for the remaining intensities 2) counting on the worth of gamma correction issue, the mapping between values within the input and output pictures could be nonlinear as an example, the worth half way between low and high may map to a price either bigger than our but the worth half way between bottom and prime.

Gamma is any worth between zero and eternity. If gamma is (the default, mapping is linear. If gamma is a smaller amount done one, the mapping is weighted toward higher (brighter) output values. If gamma is bigger than 1, the mapping is weighted towards lower (darker) output values below illustrates this relationship.

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The three transformation curves (a), (b), (c) show however values area unit mapped once gamma is a smaller amount then equal to, and bigger than 1. Smoothing operation: the smoothing operation given in [1] is followed here: to begin with, the input distinct signal is denoted by g and also the results of smoothing by f the static counts amplitude changes discretely written as below: c(f) = #fp-fp+1 (3) wherever p andp+1 area unit indices of the neighboring samples (or pixel). | fp – fp+1| could be a gradient w.r.t. p within the type of forward distinction. The numeration operator is denoted by #, that outputs the quantity of p that satisfy |fp – fp+1|! = zero, that is, the L0 norm of gradient. That is nothing however the whole range of non-zero parts. The operate c(f) doesn’t estimate gradient magnitude, and hence won’t be affected if a footing alerts its distinction. This distinct numeration operate is central to the static. Note that the live c(f) alone isn’t useful. Its combined in our technique with a general constrain that is, the result ought to be structurally the same as the signaling g to totally exhibit the competency. We tend to categorize the particular objective operate as c(f) = k indicates that k non zero radiance exist within the result. Eq. (4) is incredibly powerful to abstract the maximum amount as potential the whole energy its determined golf stokes edges elsewhere solely raises the value. This smoothing result is clearly dissimilar to those of previous edge preserving strategies. A bigger k yields finer approximation, still categorizing the foremost outstanding distinction region. Because the value in relative atomic mass. (4) stems from the quadratic intensity distinction term (fp – gp) a pair of, it’s not allowed that lot of pixels drastically structural info. The result signal flattens the details and sharpens main edges. The general form is additionally in line with the first one as a result of intensity modification should arise on important edges to cut back modification their color low amplitude structures. Therefore, are primarily removed during a governalble and applied math manner decreasing salient edges is mechanically prevented a remarkable feature of this framework is that however k is about, no edge fuzziness is caused. In practice k in eq(4) may range from tens to thousands, especially in 2D images with different resolution.

a) b)

c) d)

e) f)

Rain detected images:

a) b)

c) d)

e) f)

Fig. (a), (c) and (e) are the rain components from the original images, (b), (d) and (f) are the respective components left after applying the proposed method.

V. EXPERIMENTAL ANALYSIS

Experiments were carried out on pictures which were captured during heavy rain as shown in the figure a, c and e contain pictures which were captured in heavy rain. The figures b, d and f are the corresponding images obtained by a technique called smoothing. The above mentioned figures implies the components of rain derived by k means in original and rain removal images corresponding to those in figures. An analysis was performed which is known as connected components analysis. From the simulation results and connected components analysis one can observe the removal of rain streaks in a captured image. The pictures are not blurred and the undesirable ghost effects are not created. In addition to that the edges are well maintained and enhanced better visual quality the code was executed in Mat lab 2013 on an i7 processor 3.4 GHz and 4GB ram. It takes approximately three seconds to process an image of size 280*340 and output the rain removed image.

VI. CONCLUSION

This paper has attempted to solve the rain/snow – removing problem from a picture by efficiently utilizing the common characteristics of rain and snow. This method is independent of the local properties such as spatial and chromatic but, globally retains and sharpens the salient regions. The edges are not blurred due to the absence of local filtering and the most important operation named averaging operations. The contrast enhancement of a smoothed images is better quality images. Extensive simulation results on a variety of a captured picture which was captured in heavy rain conditions have proved that the proposed method gives best results. Finally, we have presented a huge set of results to show that our method can remove rain or snow from images effectively. Leading to an enhanced visual quality in the rain or snow –removed images.

7.REFERENCES

[1]. L.-W. Kang, C.-W. Lin, and Y.-H. Fu, “Automatic single-image-based rain streaks removal via image decomposition,” IEEE Trans. Image Process., vol. 21, no. 4, pp. 1742–1755, Apr. 2012.

[2]. J. Bossu, N. Hauti?re, and J.-P. Tarel, “Rain or snow detection in image sequences through use of a histogram of orientation of streaks,” Int. J. Comput. Vis., vol. 93, no. 3, pp. 348–367, Jul. 2011.

[3]. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res., vol. 11, pp. 19–60, Mar. 2010.

[4]. Y. Pang, J. Cao, and X. Li, “Learning sampling distributions for efficient object detection,” IEEE Trans. Cybern., vol. 47, no. 1, pp. 117–129, Jan. 2017.

[5]. Yeganeh, Hojat, Ali Ziaei, and Amirhossein Rezaie. “A novel approach for contrast enhancement based on histogram equalization.” Computer and Communication Engineering, 2008. ICCCE 2008. International Conference on. IEEE, 2008.

[6]. K. Garg and S. K. Nayar, “Detection and removal of rain from videos,” IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 528–535. 2004.

[7]. Xu, Li, et al. “Image smoothing via L0 gradient minimization.” ACM Transactions on Graphics (TOG). Vol. 30. No. 6. ACM, 2011.

[8]. Fu, Yu-Hsiang, et al. “Single-frame-based rain removal via image decomposition.” Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. IEEE, 2011.

[9]. Shen, Minmin, and Ping Xue. “A fast algorithm for rain detection and removal from videos.” Multimedia and Expo (ICME), 2011 IEEE International Conference on. IEEE, 2011.

[10]. Kang, Li-Wei, et al. “Self-learning-based rain streak removal for image/video.” Circuits and Systems (ISCAS), 2012 IEEE International Symposium on. IEEE, 2012.

[11]. H.Hase, K. Miyake, M. Yoneda, Real-time snowfall noise elimination, in: Proceedings of the IEEE International Conference on Image Processing, Springer-Verlag, Berlin, Heidelberg, 2008, pp. 451–458.

[12]. Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25, 713–724 (2003).

[13]. Tarel, J.P., Hauti`ere, N., Caraffa, L., Cord, A., Halmaoui, H., Gruyer, D.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst.Mag. 4, 6–20 (2012).