Lossy image compression allows for efficient storage and transfer of image data with varying degrees of image degradation. However, lossy compression is not commonly used in medical imaging as the process may irreversibly remove information that defines clinically important image features. The lossy component of JPEG compression is represented as lost precision in the discrete cosine transform (DCT) domain after quantization on $8 \times 8$ image blocks and results in degradation of the image. We propose a cross-domain cascade of U-nets called the W-net. This network operates in the DCT domain to restore discarded DCT coefficients that leverages information from adjacent blocks, and the image domain to suppress compression artifacts at the image pixel level. For comparison, we adapted the Automated Transform by Manifold Approximation (AUTOMAP) method for JPEG decompression by learning the dequantization of individual $8 \times 8$ DCT coefficient blocks. These results were then transformed to the image domain and processed by a U-net. The deep learning models were able to suppress common compression artifacts at the expense of high spatial frequency detail. Both the W-net and AUTOMAP network structures were quantitatively superior to standard JPEG decompression, with the W-net outperforming AUTOMAP, suggesting that leveraging DCT coefficients from adjacent blocks improves JPEG decompression performance.

中文翻译：

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使用跨域神经网络恢复有损 JPEG 压缩的脑 MR 图像

有损图像压缩允许在不同程度的图像退化的情况下有效地存储和传输图像数据。然而，有损压缩在医学成像中并不常用，因为该过程可能会不可逆转地删除定义临床重要图像特征的信息。JPEG 压缩的有损分量表示为离散余弦变换 (DCT) 域中的精度损失，在对 $8\times 8$ 图像块进行量化后，会导致图像质量下降。我们提出了一个跨域级联的 U-net，称为 W-net。该网络在 DCT 域中运行以恢复丢弃的 DCT 系数，该系数利用来自相邻块的信息，并在图像域中抑制图像像素级别的压缩伪影。为了比较，我们通过学习单个 $8\times 8$ DCT 系数块的去量化，将流形近似自动变换 (AUTOMAP) 方法应用于 JPEG 解压缩。然后将这些结果转换到图像域并由 U-net 处理。深度学习模型能够以高空间频率细节为代价来抑制常见的压缩伪影。W-net 和 AUTOMAP 网络结构在数量上都优于标准 JPEG 解压缩，W-net 的性能优于 AUTOMAP，这表明利用相邻块的 DCT 系数提高了 JPEG 解压缩性能。深度学习模型能够以高空间频率细节为代价来抑制常见的压缩伪影。W-net 和 AUTOMAP 网络结构在数量上都优于标准 JPEG 解压缩，W-net 的性能优于 AUTOMAP，这表明利用相邻块的 DCT 系数提高了 JPEG 解压缩性能。深度学习模型能够以高空间频率细节为代价来抑制常见的压缩伪影。W-net 和 AUTOMAP 网络结构在数量上都优于标准 JPEG 解压缩，W-net 的性能优于 AUTOMAP，这表明利用相邻块的 DCT 系数提高了 JPEG 解压缩性能。