In this paper, we present a novel no-reference blur metric for images. The blur metric is based on analyzing image features include the mean value of phase congruency image, the entropy of phase congruency image and the distorted image, and the gradient of the distorted image. The new index does NOT need any information from reference image, and image quality estimation is accomplished by simple functional relationship between those features. Our experimental results show that the new index outperforms existing popular no-reference blurriness metric and full reference PSNR on LIVE Gaussian blurred database and IVC blurring images.
Hyperspectral Image (HSI) classification is an important task in the field of Hyperspectral Image processing. However, the existing classification methods unable to solve the problems caused by hyperspectral image information redundancy, insufficient image feature utilization and Hughes phenomenon. Aiming at these three problems, a hyperspectral image classification algorithm based on deep learning is proposed. The Multiscale Convolutional Neural Network (MCNN) was used to excavate deep features and realize the learning of multiscale features. Then, the features of different scales were fused and classified. The results show that the proposed algorithm has higher classification accuracy than the traditional ones. Also, it has strong generalization ability and robustness. The effectiveness and feasibility of the proposed algorithm are fully verified.
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