Compressive image super-resolution

TLDR: A new algorithm to generate a super-resolution image from a single, low-resolution input without the use of a training data set is proposed by exploiting the fact that the image is highly compressible in the wavelet domain and leveraging recent results of compressed sensing theory.

Abstract: This paper proposes a new algorithm to generate a super-resolution image from a single, low-resolution input without the use of a training data set. We do this by exploiting the fact that the image is highly compressible in the wavelet domain and leverage recent results of compressed sensing (CS) theory to make an accurate estimate of the original high-resolution image. Unfortunately, traditional CS approaches do not allow direct use of a wavelet compression basis because of the coherency between the point-samples from the downsampling process and the wavelet basis. To overcome this problem, we incorporate the downsampling low-pass filter into our measurement matrix, which decreases coherency between the bases. To invert the downsampling process, we use the appropriate inverse filter and solve for the high-resolution image using a greedy, matching-pursuit algorithm. The result is a simple and efficient algorithm that can generate high quality, high-resolution images without the use of training data. We present results that show the improved performance of our method over existing super-resolution approaches.