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Proceedings Paper

Super-resolution total-variation decoding of JPEG-compressed image data
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Paper Abstract

In a digital camera, its output image is sometimes corrupted by additive noise heavily and its noisy image is often compressed with the JPEG encoder. When the coding rate of the JPEG encoder is not high enough, in a JPEG-decoded image there appear noticeable artifacts such as the blocking, the ringing, and the false color artifacts. In the high ISOsensitivity case, even if the coding rate is very high, camera's noise will produce noticeably annoying artifacts in a JPEG-decoded image. This paper presents a restoration-type decoding approach that recovers a quality-improved image from the JPEG-compressed data, while not only suppressing the occurrence of the coding artifacts particular to the JPEG compression but also removing the camera's noise to some extent. This decoding approach is a kind of superresolution image-restoration approach based on the TV (Total Variation) regularization; to reduce the ringing artifacts near sharp edges it selectively restores the DCT coefficients truncated by the JPEG compression, whereas in an originally smooth image region it flattens unnecessary signal variations to eliminate the blocking artifacts and the camera's noise. Extending the standard ROF (Rudin-Osher-Fetami) framework of the TV image restoration, in this paper we construct the super-resolution approach to the JPEG decoding. By introducing the JPEG-compressed data into the fidelity term of the energy functional and adopting a nonlinear cost function constrained by the JPEG-compressed data softly, we define a new energy functional whose minimization gives the super-resolution JPEG decoding.

Paper Details

Date Published: 20 February 2007
PDF: 12 pages
Proc. SPIE 6502, Digital Photography III, 65020G (20 February 2007); doi: 10.1117/12.702977
Show Author Affiliations
Takahiro Saito, Kanagawa Univ. (Japan)
Takashi Komatsu, Kanagawa Univ. (Japan)

Published in SPIE Proceedings Vol. 6502:
Digital Photography III
Russel A. Martin; Jeffrey M. DiCarlo; Nitin Sampat, Editor(s)

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