多值压缩编码孔径超分辨率成像方法

针对近期提出的基于压缩感知(CS,compressed sensing)理论的压缩编码成像方法在重建后引入较多类似于噪声的伪影(artitacts)问题,为了使压缩编码成像方法获得更好成像质量的图像,本文提出一种改进的压缩编码成像方法。本文方法将多值模板(MVM)代替二值模板来增强编码质量,并利用自适应全变分(TV,total variation)去噪方法去除重建后的高分辨率图像的伪影。实验结果表明,这种方法很好地改进了压缩编码孔径(CCA)的成像质量,并且大幅提高了图像的信噪比(SNR)。

Super-resolution imaging method using multi-value compressed coded aperture

A new compressed coded aperture(CCA) imaging method based on compressed sensing(CS) theory is developed to acquire high resolution image from a low resolution focus plane array(FPA) device using super resolution(SR) reconstruction algorithms.To obtain the better quality images,an improved compressed coded aperture is employed in imaging equipment.In this method,we utilize a multi-value mask(MVM) instead of the conventional binary mask in imaging system to enhance the coding quality.Then the adaptive total variation(TV) denoising algorithm is further applied to eliminate the artifacts caused by reconstruction algorithm in high-resolution image.The simulation results indicate that this algorithm can improve the imaging quality,significantly increasing the signal-to-noise-ratio(SNR) as well.