A computationally efficient superresolution image reconstruction algorithm

By Nhat Nguyen, Peyman Milanfar, Gene H. Golub. IEEE Transactions on Image Processing, 2001

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Image super-resolution: The techniques, applications, and future (2016, methodology): “It has been noted in the earlier studies that the GCV method tends to give unsatisfactory results if the model errors are highly correlated [121].”
Super-resolution: a comprehensive survey (2014, methodology): “4 [30], [36], [37], [61], [84], [85], [90], [91], [94], [105], [112], [113], [127], [128], [136], [140], [151], [157], [160], [164], [165], [167], [174], [180], [187], [188], [189], [191], [192], [193], [194], [200], [207], [208], [217], [226], [227], [228], [235], [248], [253], [259], [257], [270],”
Advances and challenges in super‐resolution (2004, methodology): “Although the crossvalidation method can be used to determine the parameter values for the nonrobust Super-Resolution method (Nguyen et al., 2001a) , a computationally efficient way of implementing such method for the robust Super-Resolution case has not yet been addressed.”
Super-resolution image reconstruction: a technical overview (2003, methodology): “Another effort concerning this issue is found in [49]-[51].”