Image_Super-Resolution_using_CNN:使用深度学习的图像超分辨率

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  • 2022-04-27 06:58
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Image_Super-Resolution_using_CNN 使用深度学习的图像超分辨率 深度卷积模型在执行图像超分辨率方面具有优势,因为SRCNN可以实现最高的PSNR(峰值信噪比)。 它是信号的最大可能功率与噪声破坏功率的比值,影响信号表示的保真度。SRCNN中的深度卷积模型无需进行预处理即可直接学习低分辨率和高分辨率图像之间的端到端映射。 。 该模型已实现了比现有方法更好的性能。 还可以相信,通过试验更多的过滤器和不同的策略,可以实现更高的性能。 而且,由于模型的鲁棒性和简单性,它也可以用于各种低级视觉问题。 PSNR是峰值信噪比(PSNR),它定义为信号的最大可能功率与影响信号表示保真度的破坏噪声的功率之比。如果PSNR的值较高,则模型越好从低分辨率图像重建高分辨率图像。 地面真像 HR-BI(PSNR = 20.497630181368823) HR-SRCNN(P
Image_Super-Resolution_using_CNN-main.zip
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内容介绍
# Image_Super-Resolution_using_CNN Image Super-resolution Using Deep Learning Deep Convolutional Model is superior to perform image super-resolution because SRCNN achieves the highest PSNR (Peak Signal to Noise Ratio). It is a ratio of the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation.The deep convolutional model in SRCNN directly learns anend-to-end mapping between low and high-resolution images with little preprocessing. This model has achieved superior performance than the state of art methods. It is also believed that more performance can be achieved by experimenting withmore filters and different strategies. Moreover, with the robustness and simplicity of the model it canalsobe used in various low-level vision problems. PSNR is the Peak signal-to-noise ratio (PSNR) is defined as theratio ofthe maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation.If the value of the PSNR is high the betteristhe model to reconstruct a high-resolution image from a low resolution image. Ground-Truth Image ![image](https://user-images.githubusercontent.com/51491512/113520735-32caa200-958d-11eb-9ecd-d59ad39408ff.png) HR-BI (PSNR =20.497630181368823) ![image](https://user-images.githubusercontent.com/51491512/113520768-60afe680-958d-11eb-942a-79268c930fee.png) HR-SRCNN (PSNR=22.922696428588342) ![image](https://user-images.githubusercontent.com/51491512/113520772-67d6f480-958d-11eb-9410-ec257aafde47.png) (PSNR for HR_image and LR_image is : 20.497630181368823 PSNR for HR_image and SR_image is : 22.922696428588342 )
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