关键词: 图像域分块, 自适应阈值, 高光谱图像, HMRF模型, 主成分变换

Abstract:

The detailed information in super-resolution reconstruction of hyper-spectral image is usually lost after using the Maximum A Posteriori (MAP). To improve the quality of a reconstructed image, this paper presents a MAP block super-resolution reconstruction algorithm based on the prior Huber Markov Random Field (HMRF) model. Firstly, Principal Component Analysis (PCA) is used to obtain the main components for a given hyper-spectral image, and then the initial image is obtained by spline interpolation technique. By using main components from the PCA operation, the proposed algorithm can not only effectively reduce the usage of computation memory but also reserve most of the information from the image. After calculating the Q statistic of the initial image, it is found that stratifying the hyper-spectral image into several (e.g., seven in this study) spatial heterogeneities is an effective way to characterize the complexity of the hyper-spectral image. To this end, a suitable partitioning scheme for obtaining an optimal super-resolution reconstructed image is adopted after comparing the reconstructed results by using different blocks with different sizes. As a result, the domain of the hyper-spectral image is split into several sub-blocks. The HMRF model with an adaptive threshold is then established for each sub-block image, and an objective function is defined by combining the fidelity terms of the sub-block images. The objective function can be solved by using the gradient descent method to obtain the high resolution sub-block images, which are then combined with the interpolated secondary component images. Though some cross artifacts occur in the process, they can be removed by extending edge based methods. The effective extending edge-based method is also proposed in this paper. Finally, the final high resolution image can be obtained by using the inverse PCA operation. In order to verify the validity and the superiority of the proposed algorithm, we test the proposed algorithm, the representative Tikhonov-based algorithm, total variation-based algorithm, and the traditional HMRF model-based super-resolution reconstruction method with the simulated and real images, respectively. The testing results show that the proposed algorithm is superior to other methods in the peak signal-to-noise ratio (PSNR) and the Structure Similarity Image Measure (SSIM).The qualitative evaluation indicated that the proposed method could obtain more obvious edge structure and detailed information at the same time.

Key words: image segmentation, adaptive threshold, hyperspectral image, HMRF model, principal component transformation