Image up-scaling employs various polynomial interpolation schemes for their reduced computational complexity and suitability for various real-time applications. However, they give blurring artifacts in up-scaled images due to the loss of high frequency (HF) information. Likewise, most of the other edge directed and transform domain interpolation schemes available in the literature though produce lesser blurring as compared to polynomial interpolation schemes but are computationally more complex. To overcome these problems, an iterative spatial domain 2-D signal decomposition technique is proposed. It is meant for extracting the very high frequency (VHF) information from a low resolution (LR) image. The VHF information is obtained by performing the signal decomposition for an estimated number of iterations. Subsequently, the superimposition of this VHF extract with the low resolution image prior to image up-scaling reduces the blurring in its up-scaled counterpart. Since the degradation of higher order sub-band information such as HF and VHF is more than the low and medium frequency information during an up-scaling process, restoration of the most degraded VHF sub-band information would produce much lesser blurring. Simulation results reveal that the proposed scheme gives better performance than many of the existing schemes in terms of objective and subjective measures.

图像放大采用各种多项式插值方案，以降低计算复杂度，并适合各种实时应用。但是，由于高频（HF）信息的丢失，它们会在放大的图像中产生模糊的伪像。同样，与多项式插值方案相比，文献中可用的大多数其他边缘定向和变换域插值方案虽然产生的模糊较少，但在计算上更为复杂。为了克服这些问题，提出了一种迭代的空间域二维信号分解技术。它旨在从低分辨率（LR）图像中提取甚高频（VHF）信息。通过对估计的迭代次数执行信号分解，可以获得VHF信息。后来，在图像按比例放大之前，将此VHF提取物与低分辨率图像叠加可减少其按比例放大的副本中的模糊。由于在升频过程中，高阶子带信息（例如HF和VHF）的降级大于低频和中频信息，因此恢复降级最严重的VHF子带信息将产生更少的模糊。仿真结果表明，该方案在客观和主观方面都比许多现有方案具有更好的性能。恢复最退化的VHF子带信息将产生更少的模糊。仿真结果表明，该方案在客观和主观方面都比许多现有方案具有更好的性能。恢复最退化的VHF子带信息将产生更少的模糊。仿真结果表明，该方案在客观和主观方面都比许多现有方案具有更好的性能。