| Presentation | 2023-03-02 QR code image dnoising netwroks based on decodability assessment Kazumitsu Takahashi, Makoto Nakashizuka, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this paper, an image denoising method for QR code images is proposed. The image recovery from the degraded QR code image is performed to improve the successful rate of the data decoding from the QR code images. In many image recovery algorithms, which include image denoising, the objective of the algorithm is to decrease the mean square error between the clean image and the recovered image. However, the decrement of the mean square error is not related to improve the decoding rate directly. There exist any possible metric of the recovery that can improve the decoding rate. In this paper, the CNN (convolutional neural network) is applied to decodability assessment of the QR code and the trained network is employed to train the denosing network that improve the decoding rate of QR codes. In the proposed method, the CNN is trained to predict success or fail of decoding of the existing QR code decoder. The output of this CNN is defined as the decodability. Then, the denosing CNN is trained to minimize the loss function that consists of this decodability and the fidelity of the output image. In experiments, the decodability assessment of the CNN is demonstrated. We show that the decoding rate obtained from the denoising neural network that is trained with the decodability is superior to the denoising neural network trained with only mean square error. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | convolutional neural network / denoising / QR code / image recovery / decode |
| Paper # | SIS2022-46 |
| Date of Issue | 2023-02-23 (SIS) |
| Conference Information | |
| Committee | SIS |
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| Conference Date | 2023/3/2(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Chiba Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Tomoaki Kimura(Kanagawa Inst. of Tech.) |
| Vice Chair | Naoto Sasaoka(Tottori Univ.) / Hakaru Tamukoh(Kyushu Inst. of Tech.) |
| Secretary | Naoto Sasaoka(NTT) / Hakaru Tamukoh(Kansai Univ.) |
| Assistant | Yoshiaki Makabe(Kanagawa Inst. of Tech.) / Yosuke Sugiura(Saitama Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Smart Info-Media Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | QR code image dnoising netwroks based on decodability assessment |
| Sub Title (in English) | |
| Keyword(1) | convolutional neural network |
| Keyword(2) | denoising |
| Keyword(3) | QR code |
| Keyword(4) | image recovery |
| Keyword(5) | decode |
| 1st Author's Name | Kazumitsu Takahashi |
| 1st Author's Affiliation | Chiba Institute of Technology(CIT) |
| 2nd Author's Name | Makoto Nakashizuka |
| 2nd Author's Affiliation | Chiba Institute of Technology(CIT) |
| Date | 2023-03-02 |
| Paper # | SIS2022-46 |
| Volume (vol) | vol.122 |
| Number (no) | SIS-410 |
| Page | pp.pp.33-36(SIS), |
| #Pages | 4 |
| Date of Issue | 2023-02-23 (SIS) |