In this paper, we introduce a ghost free High Dynamic Range imaging algorithm for obtaining ghost-free high dynamic range (HDR) images. The existing multiple image fusion based HDR method work only on condition that there is no camera and object movement when acquiring multiple, differently exposed LDR images. To overcome such an unrealistic condition, the proposed algorithm make three LDR images from a single input image. For this purpose a histogram separation method is proposed in the algorithm for generating three LDR images by stretching each separated histogram. An edge-preserving denoising technique is also proposed in the algorithm to suppress the noise that is amplified in the histogram stretching process. Because the proposed algorithm self-generates three LDR images from a single input image, ghost artifacts that are the result of the relative motion between the camera and objects during different exposure time, are removed from HDR images. Therefore, the proposed algorithm can be applied to mobile phone camera and a consumer compact camera to provide the ghost artifacts free HDR images in the form of either inbuilt or post-processing software application.
Keywords—High dynamic range imaging; HDR; LDR; Histogram stretching; Edge preserving denoising
INTRODUCTION
Acquisition of real world scenes becomes easier for non-experts since high-quality imaging devices are increase in consumer electronics market. Three essential factors for real world scenes acquisition include; i) high spatial resolution, ii) true color reproduction, and iii) high dynamic range (HDR). HDR imaging method has newly emerged in recent years and played a significant role in bringing a new revolution to digital imaging [1]. While human eye can recogn...
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...thms for function optimization [D],” Edmonton University of Alberta, 1981.
[11] J. N.Kapur, P. K Sahoo and A. K. C Wong, “A New Method for Picture Thresholding Using the Entropy of the Histogram, Computer Vision, Graphics and Image Processing,” Vol. 29, No. 3, pp. 273-285, 2007.
[12] R. A. Hummel, “Image enhancement by histogram transformation, Computer Graphics and Image Processing,”.vol.6, no.2, pp.184-195, 1977.
[13] S. Kim, E. Lee, V. Maik, and J. Paik, “Real-time image restoration for digital multifocusing in a multiple color-filter aperture camera,” Optical Engineering, vol. 49, no. 4, pp. 040502(1-3), April 2010.
[14] Jaehyun Im, Jaehwan Jeon, Monson H Hayes, “Single image-based ghost-free high dynamic range imaging using local histogram stretching and spatially-adaptive denoising,” Consumer Electronics, IEEE Transaction, Vol.57, pp. 1478—1484, November 2011.
Gustavon, Todd. Camera: A History of Photography from daguerreotype to Digital. New York, NY: Sterling Publishing, 2009. Intro p.2
by the internal computers of the instrument, to create an image of internal body tissues. These images were then displayed on the screen for the user,
... middle of paper ... ... Brown, P., & Levinson, S. C. (1987).
Dubey, R.B., et al. “The Current CAD and PACS Technologies in Medical Imaging.” International Journal of Applied Engineering Research 4.8 (2009): 1439-1456. Academic Search Complete. Web. 20 Feb. 2011.
Adkins-Covert, Tawnya J. Manipulating Images. Lanham, MD: The Rowman & Littlefield Publishing Group, Inc., 2011. book.
5. Royal Philips Electronics, Imaging : its digital future, Briefing, Volume 3, Issue 2, article no. 19.
ed. Vol. 2. New York: Harry N. Abrams, Inc., 1995. 973-974. Yaeger, Bert D. The
[5] W.Zhang, S.Shan, ”Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition,” ICCV, vol. 1, pp.786-791, 2005.
...ting the disparity map was based on belief propagation and mean shift segmentation [19]. The disparity map and the reference image (JI_L) are segmented into some objects. The objects and the average disparity of these objects are denoted by O_(JI_L)^i & d_(JI_L)^i, respectively, i = 1,2,…,m. If d_(JI_L)^i is in [D_b,D_f ], O_(JI_L)^i is regarded as the main content, O_(JI_L)^i∈O_maipart.If d_(JI_L)^iis not in [D_b,D_f ], O_(JI_L)^i is regarded as the background, O_(JI_L)^i∈O_background. That is,
Ed. Lee A. Jacobus, Ph.D. 3rd ed. of the year. Boston: Bedford Books, 1996. 672-709. 2.
The 2D video to 3D has great practical significance. First, it can lead to more intense visual stimulation to the audience, to increase realism. Secondly, 2D to 3D 3D video capture is also able to reduce costs in the process, realize the possibilities of ordinary camcorders shoot 3D video. In addition, 2D to 3D, 3D video will meet the need...
Greenblatt and M. H. Abrams. 8th ed. Vol. 2. New York: W.W. Norton, 2006. 1891
The 3-D DWT can be considered as a combination of three one dimensional DWT in the x, y and z directions, as shown in Fig. 3.1. The preliminary work in the DWT processor design is to build 1-D DWT modules, which are composed of high-pass and low-pass filters that perform a convolution of filter coefficients and input pixels. After a one-level of - discrete wavelet transform, the volume of image is decomposed into HHH, HHL, HLH, HLL, LHH, LHL, LLH and LLL signals as shown in the Fig. 3.1 [1].
Image intensification is the process of converting x-ray into visible light. “Early fluoroscopic procedures produced visual images of low intensity, which required the radiologist's eyes to be dark adapted and restricted image recording. In the late 1940s, with the rapid developments in electronics and borrowing the ideas from vacuum tube technology, scientists invented the x-ray image intensifier, which considerably brightened fluoroscopic images” (Wang & Blackburn, 2000, np). We will explore the image-intensification tube, the various gain parameters associated with the tube, and the magnification mode of the image intensifier.
[23] S.S. Tamboli1 et.al. “Image Compression Using Haar Wavelet Transform”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, 2013