用生成对抗网络修复运动模糊照片，效果清晰速度快。
Generative Adversarial Network on Motion-Blur Image Restoration
- 基于GAN框架，通过对抗训练恢复模糊图像
- 在GoPro数据集上达到29.16分PSNR和0.7459分SSIM
- 适合图像增强、摄影后期等实际应用
日常拍摄中，因手抖或快速移动导致照片出现运动模糊，严重影响画质。本文针对此问题，采用生成对抗网络（GAN）构建基于TensorFlow的去模糊模型，使用包含清晰与模糊配对图像的GoPro数据集进行训练与评估。通过判别器与生成器的对抗训练，逐步提升输出图像的真实感。采用峰值信噪比（PSNR）与结构相似性指数（SSIM）作为量化评价指标，实验结果显示平均PSNR达29.1644，平均SSIM为0.7459，单张图像平均去模糊耗时4.6921秒。生成图像中模糊像素明显更锐利，展现出良好的真实场景修复效果。
原文摘要 · Abstract (English)
In everyday life, photographs taken with a camera often suffer from motion blur due to hand vibrations or sudden movements. This phenomenon can significantly detract from the quality of the images captured, making it an interesting challenge to develop a deep learning model that utilizes the principles of adversarial networks to restore clarity to these blurred pixels. In this project, we will focus on leveraging Generative Adversarial Networks (GANs) to effectively deblur images affected by motion blur. A GAN-based Tensorflow model is defined, training and evaluating by GoPro dataset which comprises paired street view images featuring both clear and blurred versions. This adversarial training process between Discriminator and Generator helps to produce increasingly realistic images over time. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are the two evaluation metrics used to provide quantitative measures of image quality, allowing us to evaluate the effectiveness of the deblurring process. Mean PSNR in 29.1644 and mean SSIM in 0.7459 with average 4.6921 seconds deblurring time are achieved in this project. The blurry pixels are sharper in the output of GAN model shows a good image restoration effect in real world applications.
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