Modern Applied Science; Vol. 9, No. 13; 2015 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education 140 Color Balance for Panoramic Images Ahmed Bdr Eldeen Ahmed Mohammed 1 , Fang Ming 1 & Ren Zhengwei 1 1 Changchun University of Science and Technology, China Correspondence: Fang Ming, Changchun University of Science and Technology, China. Tel: 86-431-8558-3331. E-mail: fangming@cust.edu.cn Received: July 30, 2015 Accepted: September 25, 2015 Online Published: November 30, 2015 doi:10.5539/mas.v9n13p140 URL: http://dx.doi.org/10.5539/mas.v9n13p140 Abstract Color correction or color balancing in multi-view image stitching is the process of correcting the color differences between neighboring views which arise due to different exposure levels and view angles. This paper concerns the problem of color balance for panoramic images. A new algorithm is presented to create visual normalization by using correlated feature points between adjacent image sequences. After image mosaicking directly, a weighted average method is used to calculate the pixel value of panoramic image Experimental result shows that the algorithm can achieve images color balance and good visual performance. Keywords: color balance, Image stitching, panoramic image 1. Introduction A panorama is a wide-angle representation of a scene and is usually built from multiple images captured at a single location with slightly different viewpoints. The multiple captured images have to be geometrically aligned and then stitched together to form the final panorama. Various commercial applications have been developed to provide panoramic image stitching functions [1] . There are two main approaches in the literature to stitch multiple images for the panorama: optimal seam finding and transition smoothing, assuming the images have been already aligned. Optimal seam finding algorithms [2-4] search for a seam in the overlapping area so that the differences between two adjacent images on the seam are minimized. The optimal seam can be found by graph-cut [2] . Dynamic programming [3] [4] or other algorithms. Then each image is copied to the corresponding side of the seam. The advantage of optimal seam finding is its low computational and memory cost, but it is very sensitive to photometric inconsistency, which appears as a global intensity or color difference between the images due to changes of scene illumination and camera responses. Color correction is thus often used before the stitching process to balance colors and luminance in the whole image sequence. A common approach is to transform the color of all the images in the sequence to match the basic image. the transform matrix across images can be represented as a linear model [5] [6] or a diagonal model [7] , in which the mapping parameters are computed from the averages of each channel over the overlapping areas or from the mapping of histograms [5] [8] .These approaches are not sensitive to the quality of geometric alignment, but the accuracy of color correction needs to be improved. Recently, Xiong et al [4] proposed a much accurate color correction algorithm that minimizes a global error function, to get the correction coefficients simultaneously for the whole image sequence, followed by a color blending step to further smooth the transition. To establish the global error function, it is necessary to extract all the mean values of overlapping areas between every pair of adjacent images. In other words, the color correction process and panorama stitching process can only be started after the complete image sequence is captured, which causes unwanted delays. In this paper, an efficient image stitching approach is proposed to address the color balance for the panoramic image. 2. Work Flow of the Algorithm In this section, the work flow of the algorithm for color balance. It mainly consists of three steps as the following and these explaining as shown in Figure 1. 1. Two images sequences from adjacent cameras with overlapped region are selected. Then we take one as a reference image and the other as the target image. 2. Feature points are extracted and matched by using SURF detector. 3. Finally, a panoramic image created and the seam line between two images is remove because this paper use