图像的超分辨力复原和信噪比的提高是图像复原追求的目标. Poisson-ML 图像复原方法(PML)具有很强的超分辨力复原能力,但在复原过程中会产生振荡条纹且对带噪较大的图像不能取得理想的复原效果. 在Poisson和Markov 分布假设的基础上,提出基于Poisson-Markov 场的超分辨力图像复原算法及其正则化参数的自适应选择方法(MPML). 实验表明,MPML 算法不但具有很好的超分辨力复原能力,而且能有效减少和去除复原图像中的振荡条纹,对于带噪较大的图像也能取得理想的复原效果,因此其图像复原质量明显好于PML 算法. 正则化参数能被自动优化地选择且与图像复原的迭代运算同步进行.

By using orthogonal discrete wavelet transform (ODWT) and generalized cross validation(GCV),and combining with Luck-Richardson algorithm based on Poisson-Markov model(MPML),several new super-resolution image restoration algorithm are proposed. According to simulation experiments for practical images, all the proposed algorithm could retain image details better than MPML, and be more suitable to low signal-to-noise ratio (SNR)images. The single operation wavelet MPML(SW-MPML) algorithm and MPML algorithm based on single operation wavelet transform(MPML-SW) avoid the iterative operation of self-adaptive parameter in MPML particularly, and improve operating speed and precision. They are instantancous to super-resolution image restoration process and have extensive application foreground.

Image expansion plays a very important role in image analysis. Common methods of image expansion, such as the zero-order hold method, may generate a visual mosaic to the expanded image, linear and cubic spline interpolation may blur the image data at peripheral regions. Since infrared images have the characteristics of low contrast and low signal-to-noise ratio (SNR), the expanded images derived from common methods are not satisfactory. As shown in the analysis of the course from images with low resolution to those with high resolution, the expansion of image is found to be an ill-posed inverse problem. An image interpolation algorithm based on MAP estimation under Bayesian framework is proposed in this paper, which can effectively preserve the discontinuities in the original image. Experimental results demonstrate that the expanded images by this method are visually and quantitatively (analyzed by using the criteria of mean squared error (MSE) and mean absolute error (MAE)) superior to the images expanded by common methods of linear interpolation. Even in expansion of infrared images, this method can also give good results. An analysis about choosing regularization parameter α in this algorithm is given.

提出夜视彩色融合图像质量评价的三个基本指标是“目标探测性”、“细节”和“色彩”,并且通过视觉评价实验,研究了“感知质量”与这三个指标的相关性. 对十个场景的微光与红外图像采用四种融合算法产生的40幅假彩色图像进行评价实验,实验结果得出:融合图像都有好目标探测性,感知质量与目标探测性的相关性很小;感知质量与细节的相关系数是0. 89, 感知质量与色彩的相关系数是0. 75. 说明在满足目标探测性要求条件下,细节是视觉感知融合图像质量的最主要评价指标,而且色彩成为夜视彩色图像质量的一个重要指标.

color night vision; image fusion; opponent vision property; low-level light visible; infrared