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TextureToMTF: predicting spatial frequency response in the wild
Signal, Image and Video Processing ( IF 2.3 ) Pub Date : 2020-02-14 , DOI: 10.1007/s11760-020-01656-w
Murtuza Bohra , Sajal Maheshwari , Vineet Gandhi

In this work, we propose an no-reference image quality assessment (NR-IQA) approach at a confluence of signal processing and deep learning. We use MTF50 (spatial frequency where modulation transfer function is 50% of its peak value) on slanted edged as a measure for image quality. We propose a comprehensive IQA dataset of images captured through hand-held phone camera in variety of situations with slanted edges around it. The MTF50 values at the slanted edges are then used to garner ground truth values for each patch in the captured images. A convolution neural network is then trained to predict MTF50 values from arbitrary image patches. We present results on the proposed dataset and synthetically generated TID2013 dataset and show state-of-the-art performance for IQA in the wild.

中文翻译:

TextureToMTF:预测野外空间频率响应

在这项工作中,我们提出了一种融合信号处理和深度学习的无参考图像质量评估 (NR-IQA) 方法。我们在倾斜边缘上使用 MTF50(调制传递函数为其峰值的 50% 的空间频率)作为图像质量的衡量标准。我们提出了一个全面的 IQA 数据集,该数据集是在各种情况下通过手持电话摄像头拍摄的,周围有倾斜的边缘。然后使用倾斜边缘的 MTF50 值为捕获的图像中的每个补丁获取地面实况值。然后训练卷积神经网络从任意图像块中预测 MTF50 值。我们展示了提议的数据集和综合生成的 TID2013 数据集的结果,并展示了 IQA 在野外的最先进性能。
更新日期:2020-02-14
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