Image enhancement using divide-and-conquer strategy |
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| Institution: | 1. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran;2. Electrical Engineering Department, Semnan University, Semnan, Iran;3. Computer Science and Engineering Department, University of California, San Diego, CA, USA;1. Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea;2. State Key Laboratory of ISN, Xidian University, Xi’an 710071, China;3. Ming Hsieh Department of Electrical Engineering and the Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA;1. Faculty of Science, Engineering, and Computing, Kingston University, London, United Kingdom;2. Community College, Qassim University, Buraydah, Saudi Arabia;3. School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom;1. The Islamia University of Bahawalpur, Department of Computer Science & Information Technology, Pakistan;2. University of Essex, Colchester, United Kingdom |
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| Abstract: | Existing enhancement methods tend to overlook the difference between image components of low-frequency and high-frequency. However, image low-frequency portions contain smooth areas occupied the majority of the image, while high-frequency components are sparser in the image. Meanwhile, the different importance of image low-frequency and high-frequency components cannot be precisely and effectively for image enhancement. Therefore, it is reasonable to deal with these components separately when designing enhancement algorithms with image subspaces. In this paper, we propose a novel divide-and-conquer strategy to decompose the observed image into four subspaces and enhance the images corresponding to each subspace individually. We employ the existing technique of gradient distribution specification for these enhancements, which has displayed promising results for image naturalization. We then reconstruct the full image using the weighted fusion of these four subspace images. Experimental results demonstrate the effectiveness of the proposed strategy in both image naturalization and details promotion. |
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| Keywords: | Image enhancement Subspace decomposition Gradient distribution specification Weighted fusion |
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