基于Poisson-Markov场的超分辨力图像复原算法

Super-Resolution Image Restoration Algorithm Based on Poisson-Markov Model

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 .

The aim of image restoration is to achieve super-resolution and increase signal-noise ratio. Poisson-ML image restoration algorithm (PML) has high super-resolution performance, but introduces oscillatory artifacts in the restored image and can not get a ideal restoration result for the noisy image. Super-resolution image restoration algorithm based on Poisson and Markov model as well as the adaptive choice method of the regularization parameter (MPML) is proposed through the assumption of Poisson and Markov random field. Experiments demonstrate that MPML not only has high super-resolution performance, but also can effectively reduce and remove oscillatory artifacts in restored images, and get ideal restoration result for the noisy image. The significantly improved restoration results are obtained using MPML compared with PML. The regularization parameter can be automatically and optimally chosen in step with the restoration of the degraded image.