Imaging Spectrometer Reconstruction And Texture Color An Objective Assessment Of The Study

Multispectral imaging is of great importance in color acquisition and reproduction. One reason is that multispectral images contain more spectral information; the other reason is that multispectral images can effectively eliminate the phenomenon of metamerism. This thesis presents a new method for spectral reflectance reproduction in multispectral imaging system, and introduces an image restoration method to solve the out-of-focus image blurring. We also conduct preliminary investigation on the texture effect in visual color difference evaluation.In multispectral imaging, Wiener estimation is widely adopted for the reconstruction of spectral reflectance. We propose an improved reflectance reconstruction method by adaptively selecting training samples for the autocorrelation matrix calculation in Wiener estimation, without a prior knowledge of the spectral information of the samples being imaged. The performance of the proposed adaptive Wiener estimation and the traditional method are compared in the cases of different channel numbers and noise levels. Experimental results show that the proposed method outperforms the traditional method in terms of both spectral and colorimetric prediction errors when the imaging channel number is 7 or less.Because of the different focus of the filters in different channels, out-of-focus blurring always occurs in the channels other than the one whose focus is appropriately adjusted, and therefore image restoration techniques are required to eliminate or reduce the blurring effect. We propose such an image restoration method by modify the strength parameter function of the traditional Unsharp Mask filter for the sake of both image sharpening and noise removal. The optimal parameters are decided according to a visual evaluation term called Q-value. Compared with other techniques, the proposed method can produce perceptually more clear images, which is also closer to the ideally focused one.Surface texture is becoming the most interesting factor in color quality assessment and color difference evaluation areas. This thesis gives some preliminary research results through certain analysis. During the quantitative assessment process, we acquire some statistical variances of texture features through the light direction, then pays attention to the correlation between statistical variances in the hope of eliminating some variances, simplifying the final linear regression model.