Loss-Specific Training of Random Forests for Super-Resolution

Alexander Grabner, Georg Poier, Michael Opitz, Samuel Schulter, Peter M. Roth

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Forschung › Begutachtung

Abstract

Super-resolution addresses the problem of image upscaling by reconstructing high-resolution output images from low-resolution input images. One successful approach for this problem is based on random forests. However, this approach has a large memory footprint, since complex models are required to achieve high accuracy. To overcome this drawback, we present a novel method for constructing random forests under a global training objective. In this way, we improve the fitting power and reduce the model size. In particular, we combine and extend recent approaches on loss-specific training of random forests. However, in contrast to previous works, we train random forests with globally optimized structure and globally optimized prediction models. We evaluate our proposed method on benchmarks for single image super-resolution. Our method shows significantly reduced model size while achieving competitive accuracy compared to state-of-the art approaches.
Originalspracheenglisch
TitelProceedings of the 22nd Computer Vision Winter Workshop
Redakteure/-innenWalter G. Kropatsch, Ines Janusch, Nicole M. Artner
Herausgeber (Verlag)TU Wien, Pattern Recongition and Image Processing Group
Kapitel23
Seitenumfang9
ISBN (elektronisch)978-3-200-04969-7
PublikationsstatusVeröffentlicht - 2017

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Grabner, A., Poier, G., Opitz, M., Schulter, S., & Roth, P. M. (2017). Loss-Specific Training of Random Forests for Super-Resolution. in W. G. Kropatsch, I. Janusch, & N. M. Artner (Hrsg.), Proceedings of the 22nd Computer Vision Winter Workshop TU Wien, Pattern Recongition and Image Processing Group.

Loss-Specific Training of Random Forests for Super-Resolution. / Grabner, Alexander; Poier, Georg; Opitz, Michael; Schulter, Samuel; Roth, Peter M.

Proceedings of the 22nd Computer Vision Winter Workshop. Hrsg. / Walter G. Kropatsch; Ines Janusch; Nicole M. Artner. TU Wien, Pattern Recongition and Image Processing Group, 2017.

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Forschung › Begutachtung

Grabner, A, Poier, G, Opitz, M, Schulter, S & Roth, PM 2017, Loss-Specific Training of Random Forests for Super-Resolution. in WG Kropatsch, I Janusch & NM Artner (Hrsg.), Proceedings of the 22nd Computer Vision Winter Workshop. TU Wien, Pattern Recongition and Image Processing Group.
Grabner A, Poier G, Opitz M, Schulter S, Roth PM. Loss-Specific Training of Random Forests for Super-Resolution. in Kropatsch WG, Janusch I, Artner NM, Hrsg., Proceedings of the 22nd Computer Vision Winter Workshop. TU Wien, Pattern Recongition and Image Processing Group. 2017
Grabner, Alexander ; Poier, Georg ; Opitz, Michael ; Schulter, Samuel ; Roth, Peter M. / Loss-Specific Training of Random Forests for Super-Resolution. Proceedings of the 22nd Computer Vision Winter Workshop. Hrsg. / Walter G. Kropatsch ; Ines Janusch ; Nicole M. Artner. TU Wien, Pattern Recongition and Image Processing Group, 2017.
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abstract = "Super-resolution addresses the problem of image upscaling by reconstructing high-resolution output images from low-resolution input images. One successful approach for this problem is based on random forests. However, this approach has a large memory footprint, since complex models are required to achieve high accuracy. To overcome this drawback, we present a novel method for constructing random forests under a global training objective. In this way, we improve the fitting power and reduce the model size. In particular, we combine and extend recent approaches on loss-specific training of random forests. However, in contrast to previous works, we train random forests with globally optimized structure and globally optimized prediction models. We evaluate our proposed method on benchmarks for single image super-resolution. Our method shows significantly reduced model size while achieving competitive accuracy compared to state-of-the art approaches.",
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