Super-resolution restoration of MMW image based on sparse representation method

This paper presents a new super-resolution (SR) restoration method of a single millimeter wave image (MMW), based on sparse single representation. A single MMW image is in fact a low-resolution one that is viewed as a downsampled version of a high-resolution image, whose image patches are assumed to be well represented as a sparse linear combination of elements from an appropriately chosen overcomplete dictionary. The theoretical results from compressed sensing ensure that under mild conditions, the sparse representation can be correctly restored from the downsampled signal. Inspired by this idea, a sparse representation for each image path of the MMW image with hardly low-resolution is sought, and then the coefficients of this sparse representation are used to generate the high-resolution output. The high- and low-resolution dictionary pair can be obtained by utilizing the modified fast sparse coding (FSC) algorithm, and it is a more compact representation of the image patch pairs, which hardly reduces the computational cost. In tests, image patches of several high-resolution natural images with different classes and their corresponding low-resolution versions are firstly used to train the dictionary pair. Further, utilizing the learned dictionary pair, the MMW image is restored efficiently. For the restored natural images, the image quality is measured by single noise ratio (SNR), while for the MMW image, it is not suitable to use the SNR criterion because much noise existed in the MMW image. So, the restored MMW image’s quality is measured by the relative SNR (RSNR). Compared with other SR restoration methods of bicubic, Lucy–Richardson (L–R) and neighbor embedding (NE), the simulation experimental results testify that the sparse representation is indeed effective in the super-resolution restoration task of the MMW image.