Joint generative learning and super-resolution for real-world camera-screen degradation

Guanghao Yin; Shouqian Sun; Chao Li; Xin Min

doi:10.1117/12.2680910

27 June 2023 Joint generative learning and super-resolution for real-world camera-screen degradation

Guanghao Yin, Shouqian Sun, Chao Li, Xin Min

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127050V (2023) https://doi.org/10.1117/12.2680910
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

In real-world applications, there are many scenarios where people need to capture digital screens. While the quality of digital images captured by cameras and mobile phones is constantly being improved, taking high-resolution images of digital screens remains challenging. Except for the camera sensor, the display screen also involves more complicated degradations, such as noise, color distortion, etc. However, few studies of single image super-resolution (SISR) have focused on the camera-screen degradation. In this paper, we build the first camera-screen degraded dataset (Cam- ScreenSR), where HR images are original ground truths from the previous DIV2K dataset and corresponding LR images are camera-captured versions of HRs displayed on the screen. Moreover, we propose a joint two-stage model which consists of the downsampling degradation GAN(DD-GAN) and the dual residual channel attention network (DuRCAN). Specifically, DD-GAN first learns the real degradation to synthesize more various LR images, and then DuRCAN learns to recover the mixed real and synthetic LR images supervised with paired HR ground-truths. We also use a Laplacian loss to sharpen the high-frequency edges. Extensive experiments validate that our proposed method outperforms existing SOTA models in both synthetic and real-degraded datasets. Moreover, in real captured photographs, our model also delivers the best visual quality with sharper edge, fewer artifacts, and especially appropriate color enhancement, which has not been accomplished by previous methods.

Citation Download Citation

Guanghao Yin, Shouqian Sun, Chao Li, and Xin Min "Joint generative learning and super-resolution for real-world camera-screen degradation", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127050V (27 June 2023); https://doi.org/10.1117/12.2680910

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KEYWORDS

Cameras

Lawrencium

Education and training

Data modeling

Visualization

Displays

Super resolution

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