Factors that can limit the effective resolution of an imaging system may include aliasing from under-sampling, blur from the optics and external factors, and sensor noise. Image restoration and super-resolution (SR) techniques can be used to improve image resolution. One SR method, developed recently, is the adaptive Wiener filter (AWF) SR algorithm. This is a multi-frame SR method that combines registered temporal frames through a joint nonuniform interpolation and restoration process to provide a high-resolution image estimate. Variations of this method have been demonstrated to be effective for multi-frame SR, as well demosaicing RGB and polarimetric imagery. While the AWF SR method effectively exploits subpixel shifts between temporal frames, it does not exploit self similarity within the observed imagery. However, very recently, the current authors have developed a multi-patch extension of the AWF method. This new method is referred to as a collaborative AWF (CAWF). The CAWF method employs a finite size moving window. At each position, we identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in all of the similar patches is used to estimate the center pixel in the reference patch. Like the AWF, the CAWF can perform nonuniform interpolation, deblurring, and denoising jointly. The big advantage of the CAWF, vs. the AWF, is the CAWF can also exploit self-similarity. This is particularly beneficial for treating low signal-to-noise ratio (SNR) imagery. To date, the CAWF has only been developed for Nyquist-sampled single-frame image restoration. In this paper, we extend the CAWF method for multi-frame SR. We provide a quantitative performance comparison between the CAWF SR and the AWF SR techniques using real and simulated data. We demonstrate that CAWF SR outperforms AWF SR, especially in low SNR applications
