![]() N2 - In this paper, a convolutional neural network for joint image demosaicing, denoising, deblurring, super-resolution and clarity enhancement is proposed. © 2022, Society for Imaging Science and Technology. 075-15-2021-997 of ), and Mykola Ponomarenko - the financial support of Huawei-Tampere University project 3114100158, FlexISP. Vladimir Marchuk would like to acknowledge the financial support of the Russian Federation represented by the Ministry of Science and Higher Education of the Russian Federation (Agreement No. T2 - IS and T International Symposium on Electronic Imaging: Computational Imaging A comparative analysis using the MOS demonstrates a low correspondence between image quality metrics and human perception for the clarity enhancement task.", ![]() The MOS prove that clarity enhancement can significantly increase image visual quality. Mean opinion scores (MOS) for the test set are collected. A small dataset ClarityDegr120 of color images with different clarity degradations and enhancements is designed using images processed by FiveNet. It is also demonstrated that adding clarity enhancement into the processing chain can additionally increase image quality (by up to 3-4 dB in PSNR). It is shown that the designed network FiveNet can effectively process images with the mix of five different distortions. The network inputs are four-channel Bayer CFA image (R, G, G, B) and three channels of the same size containing distortions maps, namely, noise level map, blur level map, and clarity degradation map. A comparative analysis using the MOS demonstrates a low correspondence between image quality metrics and human perception for the clarity enhancement task.Ībstract = "In this paper, a convolutional neural network for joint image demosaicing, denoising, deblurring, super-resolution and clarity enhancement is proposed. In this paper, a convolutional neural network for joint image demosaicing, denoising, deblurring, super-resolution and clarity enhancement is proposed.
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