In this paper, we propose a new algorithm to remove haze from a single input image. Based on the Dark Channel Prior proposed by He [1], we exploit the Gauss Bilateral Filter and the min operation to obtain an edge-preserving dark channel image, which is non-iterative, requires less time. We further utilize this dark channel image to extract the estimation of medium transmission, and finally recover a haze-free image from that. Furthermore, we use a self-adaptive algorithm to set the haze parameters to solve the color shift problem for large sky region. Experiments demonstrate our algorithm can effectively remove haze from a foggy image while keep edges sharp.
How to improve MR imaging speed by using Compressive Sensing (CS) theory, which reduce imaging time by sampling only a small bit of K-space data, has become a research focus of MR reconstruction. The measurement required in CS theory relies on the sparsity of sparse transform and the objective function of reconstruction. In this paper, we introduce contourlet transform into MR reconstruction based on CS, which achieves a sparser representation of image in contrast with orthogonal transforms such as wavelet. In addition, a reweighted L1minimization scheme is introduced as the objective function instead of the traditional unweighted L1-minimization to further improve reconstruction performance. The experimental results prove the effectiveness of the proposed approach.
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