Image Enhancement is one of the challenging issue in image processing. The objective of enhancement is modifying an image by removing the noise for making it easier to identify the key features. The current proficient strategy for recuperating dependable nearby arrangements of thick correspondences between two pictures with some common substance. The technique is intended for sets of pictures delineating comparable districts procured by various cameras and lenses, under non-inflexible changes, under various lighting, and over various foundations. Here use of another coarse-to-fine plan in which nearest-neighbor field calculations utilizing Generalized Patch Match are interleaved with fitting a worldwide non-direct parametric shading model and amassing reliable coordinating districts utilizing locally versatile imperatives. Contrasted with past correspondence approaches, technique joins the better of two universes: It is thick, as optical stream and stereo reproduction strategies, and it is likewise powerful to geometric and photometric varieties, sparse feature matching. This shows the convenience of technique utilizing three applications for programmed case based photo improvement: altering the tonal attributes of a source picture to coordinate a reference, exchanging a known mask to a new image, and kernel, and portion estimation for picture deblurring. The present work investigated various image enhancement techniques for noise elimination and to identify the key features of the image.

Correspondence, color transfer, Patch Match, nearest neighbor field, deblurring

[1]. An, X., And Pellacini, F. 2010. User-Controllable Color Transfer. Computer Graphics Forum 29, 2, 263–271.
[2]. Barnes, C., Shechtman, E., Goldman, D. B., And Finkelstein, A. 2010. The Generalized Patchmatch Correspondence Algorithm. In Proc. Eccv, Vol. 3, 29–43.
[3]. Lucas, B. D., And Kanade, T. 1981. An Iterative Image Registration Technique with An Application to Stereo Vision. In Proc. Darpa Image Understanding Workshop, 121–130.
[4]. Snavely, N., Seitz, S. M., And Szeliski, R. 2006. Photo Tourism: Exploring Photo Collections In 3d. Acm Trans. Graph. 25 (July), 835–846.
[5]. Liu, C., Yuen, J., Torralba, A., Sivic, J., And Freeman, W. T. 2008. Sift Flow: Dense Correspondence Across Different Scenes. In Proc. Eccv, Vol. 3, 28–42
[6]. Cho, M., Shin, Y. M., And Lee, K. M. 2008. Co-Recognition of Image Pairs by Data-Driven Monte Carlo Image Exploration. In Proc. Eccv 2008, Vol. 4, 144–157.
[7]. Rother, C., Minka, T. P., Blake, A., And Kolmogorov, V. 2006. Cosegmentation Of Image Pairs by Histogram Matching– Incorporating A Global Constraint into Mrfs. In Proc. Cvpr 2006, Vol. 1, 993–1000.
[8]. Ancuti, C., Ancuti, C. O., And Bekaert, P. 2008. Deblurring By Matching. Computer Graphics Forum 28, 2, 619–628.
[9]. [9] Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., And Gool, L. V. 2005. A Comparison of Affine Region Detectors. Int. J. Comput. Vision 65 (November), 43–72.
[10]. Bai, X., Wang, J., Simons, D., And Sapiro, G. 2009. Video Snapcut: Robust Video Object Cutout Using Localized Classifiers. Acm Trans. Graph. 28, 3 (July), 70:1–70:11.
[11]. Eisemann, M., Eisemann, E., Seidel, H.-P., And Magnor, M. 2010. Photo Zoom: High Resolution from Unordered Image Collections. In Proc. Graphics Interface, 71–78.
[12]. Using Photographs to Enhance Videos Of A Static Scene. In Rendering Techniques 2007, Eurographics, 327–338.
[13]. Cho, S., And Lee, S. 2009. Fast Motion Deblurring. Acm Trans. Graph. 28, 5 (December), 145:1–145:8.
[14]. Bhat, P., Zitnick, C. L., Snavely, N., Agarwala, A., Agrawala, M., Curless, B., Cohen, M., And Kang, S. B. 2007.
[15]. Eisemann, E., And Durand, F. 2004. Flash Photography Enhancement Via Intrinsic Relighting. Acm Trans. Graph. 23 (August), 673–678.
[16]. Reinhard, E., Ashikhmin, M., Gooch, B., And Shirley, P. 2001. Color Transfer Between Images. Ieee Comput. Graph. Appl. (September 2001).
[17]. Brox, T., Bregler, C., And Malik, J. 2009. Large Displacement Optical Flow. In Proc. Cvpr 2009, Ieee, 41–48.
[18]. Cho, M., Lee, J., And Lee, K. 2009. Feature Correspondence and Deformable Object Matching Via Agglomerative Correspondence Clustering. In Proc. Iccv, 1280–1287.
[19]. Dale, K., Johnson, M. K., Sunkavalli, K., Matusik, W., And Pfister, H. 2009. Image Restoration Using Online Photo Collections. In Proc. Iccv, Ieee.
[20]. Liu, X., Wan, L., Qu, Y., Wong, T.-T., Lin, S., Leung, C.-S., And Heng, P.-A. 2008. Intrinsic Colorization. Acm Trans. Graph. 27, 5 (December), 152:1–152:9.
[21]. Joshi, N., Matusik, W., Adelson, E. H., And Kriegman, D. J. 2010. Personal Photo Enhancement Using Example Images. Acm Trans. Graph. 29, 2 (April), 12:1–12:15.
[22]. Zelnik-Manor, L., And Irani, M. 2006. On Single-Sequence and Multi-Sequence Factorizations. Int. J. Comput. Vision 67 (May), 313–326.