Dyadic Curvelet Transform (DClet) for Image Noise Reduction
Marjan Sedighi Anaraki*, Fangyan Dong*,
Hajime Nobuhara**, and Kaoru Hirota*
*Dept. of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**Dept. of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba science city, Ibaraki 305-8573, Japan
Dyadic Curvelet transform (DClet) is proposed as a tool for image processing and computer vision. It is an extended curvelet transform that solves the problem of conventional curvelet, of decomposition into components at different scales. It provides simplicity, dyadic scales, and absence of redundancy for analysis and synthesis objects with discontinuities along curves, i.e., edges via directional basis functions. The performance of the proposed method is evaluated by removing Gaussian, Speckles, and Random noises from different noisy standard images. Average 26.71 dB Peak Signal to Noise Ratio (PSNR) compared to 25.87 dB via the wavelet transform is evidence that the DClet outperforms the wavelet transform for removing noise. The proposed method is robust, which makes it suitable for biomedical applications. It is a candidate for gray and color image enhancement and applicable for compression or efficient coding in which critical sampling might be relevant.
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