Single image super resolution and denoising using multiple wavelet domain sparsity

A computer-implemented method of enhancing one or more images, the method comprising:

• receiving one or more observed images;
• identifying a plurality of wavelet bases;
• creating a cost function which generates one or more denoised images based on the observed images and the plurality of wavelet bases, wherein the cost function applies a sparsity-inducing norm to the plurality of wavelet bases;
• and
• generating the one or more denoised images by minimizing the cost function, wherein minimization of the cost function is constrained by applying a low-pass blurring filter operator and a downsampling operator to the one or more denoised images.

A method of enhancing observed images, the method comprising:

• receiving one or more observed images;
• estimating a blurring kernel based on one or more system calibration parameters;
• determining a low-pass blurring filter operator based on the blurring kernel;
• determining a downsampling operator;
• creating a cost function which generates one or more super-resolution images based on the one or more observed images by applying a sparsity-inducing norm to a plurality of wavelet bases;
• and
• generating the one or more super-resolution images by minimizing the cost function, wherein minimization of the cost function is constrained by requiring the one or more super-resolution images to be equal to a result of applying the low-pass blurring filter operator and the downsampling operator to the one or more super-resolution images.