Example-based super-resolution

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Example-based super-resolution (2001)

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by William T. Freeman , Thouis R. Jones , Egon C. Pasztor

Venue:	IEEE COMPUT. GRAPH. APPL
Citations:	348 - 5 self

BibTeX

@ARTICLE{Freeman01example-basedsuper-resolution,
    author = {William T. Freeman and Thouis R. Jones and Egon C. Pasztor},
    title = {Example-based super-resolution},
    journal = {IEEE COMPUT. GRAPH. APPL},
    year = {2001},
    pages = {1833}
}

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Abstract

The Problem: Pixel representations for images do not have resolution independence. When we zoom into a bitmapped image, we get a blurred image. Figure 1 shows the problem for a teapot image, rich with real-world detail. We know the teapot’s features should remain sharp as we zoom in on them, yet standard pixel interpolation methods, such as pixel replication (b, c) and cubic spline interpolation (d, e), introduce artifacts or blurring of edges. For images zoomed 3 octaves, such as these, sharpening the interpolated result has little useful effect (f, g). Many applications in graphics or image processing could benefit from such pixel resolution independence, such as texture mapping, enlarging consumer photographs, and converting NTSC video content to HDTV. We don’t expect perfect resolution independence—even the polygon representation doesn’t have that—but increasing the resolution independence of pixel-based representations is an important task for image-based rendering. Our example-based super-resolution algorithm yields Fig. 1 (h, i). Previous Work: Researchers have long studied image interpolation, although only recently using machine learning or sampling approaches, which offer much power. Cubic spline interpolation [5] is a very common image interpolation function, but suffers from blurring of edges and image details. Recent attempts to improve on cubic spline interpolation [6, 8, 2] have met with limited success. Schreiber and collaborators [6] proposed a sharpened Gaussian interpolator function to minimize information

Keyphrases

cubic spline interpolation resolution independence recent attempt pixel-based representation sampling approach many application introduce artifact much power image detail image-based rendering sharpened gaussian interpolator function bitmapped image limited success blurred image image processing real-world detail example-based super-resolution algorithm yield fig pixel representation previous work teapot feature little useful effect important task ntsc video content standard pixel interpolation method perfect resolution independence consumer photograph machine learning common image interpolation function polygon representation doesn pixel replication teapot image image interpolation pixel resolution independence texture mapping interpolated result

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