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In this paper, we address the problem of natural flower classification. It is a challenging task due to the non-rigid deformation, illumination changes, and inter-class similarity. We build a large dataset of flower images in the wide with 79 categories and propose a novel framework based on convolutional neural network (CNN) to solve this problem. Unlike other methods using hand-crafted visual features,...
Color image interpolation is usually implemented by the componentwise interpolation scheme using gray image interpolation methods, which is insufficient and results in additional artifacts. In this paper, an effective color image interpolation method is proposed, which extends the classical bicubic convolution interpolation. Our improvement includes: considering the correlation between color channels;...
This paper proposes a new image denoising method BlockShrink. BlockShrink is a completely data-driven block thresholding approach and is also easy to implement. It utilizes the pertinence of the neighbor wavelet coefficients by using the block thresholding scheme. It can decide the optimal block size and threshold for every wavelet subband by minimizing Stein's unbiased risk estimate (SURE). BlockShrink...
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