Single image super-resolution from transformed self-exemplars
Jia-Bin HuangAbhishek SinghNarendra Ahuja
Develops a self-similarity super-resolution method that expands internal patch matching through perspective and affine transformations, delivering state-of-the-art reconstruction on structured scenes without requiring external training datasets.
Researchers developed an improved approach to single-image super-resolution that builds solely on recurring patches inside the input image rather than relying on large external training sets. Conventional self-similarity methods often produce pleasing results yet fall short when the internal collection of patches cannot capture the range of textures and shapes present in structured scenes such as buildings or cityscapes. The work therefore set out to enlarge the effective internal dictionary by allowing controlled geometric changes to candidate patches while preserving the advantage of operating without outside data.
The authors first detect planar surfaces and their perspective geometry in the scene, then apply those transformations to guide patch search across scales. They further compose these perspective corrections with additional affine adjustments that accommodate small local shape variations. Performance was assessed on both urban images containing strong planar structure and on natural scenes, using quantitative measures such as peak signal-to-noise ratio and structural similarity together with visual inspection at magnification factors of three, four, and eight.
The method yields noticeably higher fidelity on urban scenes, improving peak signal-to-noise ratio by roughly one decibel over leading external-dictionary techniques while producing sharper edges and more faithful reproduction of fine architectural details. On natural scenes the results remain comparable to state-of-the-art alternatives. The approach also generates a new public benchmark of urban images that supports reproducible evaluation of super-resolution algorithms.
These outcomes indicate that modest geometric expansion of an image’s own patch collection can rival or exceed the performance of far larger external databases when the scene contains regular structure, thereby lowering data and compute requirements for practical deployment. The gains are most pronounced precisely where existing internal methods have been weakest, suggesting immediate utility in applications such as urban mapping or architectural photography.
Further validation on wider scene categories and integration with lightweight external priors would strengthen generalizability. The current evaluation is limited to the scenes and magnification factors shown; readers should therefore treat the reported margins as indicative rather than universal until larger-scale testing confirms consistency across varied imaging conditions.
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