Lab. for Image and Signal Anal., Notre Dame Univ., IN.
IEEE Trans Image Process. 1995;4(8):1109-19. doi: 10.1109/83.403416.
Multispectral images consist of multiple channels, each containing data acquired from a different band within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. Due to often harsh imaging environments, the acquired data may be degraded by both blur and noise. Simply applying a monochromatic restoration algorithm to each frequency band ignores the cross-channel correlation present within a multispectral image. A Gibbs prior is proposed for multispectral data modeled as a Markov random field, containing both spatial and spectral cliques. Spatial components of the model use a nonlinear operator to preserve discontinuities within each frequency band, while spectral components incorporate nonstationary cross-channel correlations. The multispectral model is used in a Bayesian algorithm for the restoration of color images, in which the resulting nonlinear estimates are shown to be quantitatively and visually superior to linear estimates generated by multichannel Wiener and least squares restoration.
多光谱图像由多个通道组成，每个通道包含从频谱的不同频带中获取的数据。由于大多数物体在很大的光谱带宽内发射或反射能量，因此通道之间通常存在显著的相关性。由于成像环境通常很恶劣，因此采集的数据可能会受到模糊和噪声的影响。简单地将单色恢复算法应用于每个频带会忽略多光谱图像中存在的跨通道相关性。对于建模为马尔可夫随机场的多光谱数据，提出了一种吉布斯先验。该模型包含空间和光谱团块。模型的空间分量使用非线性算子来保持每个频带内的不连续性，而光谱分量则包含非平稳的跨通道相关性。该多光谱模型用于彩色图像的恢复的贝叶斯算法中，结果表明，生成的非线性估计在定量和视觉上都优于多通道维纳和最小二乘恢复生成的线性估计。
