閱讀論文《Loss Functions for Image Restoration With Neural Networks》
這是去年計算成像學領域的一篇論文（連接：https://ieeexplore.ieee.org/document/7797130/ ），重在討論如何損失函數對圖像恢復的影響。在目前的圖像恢復任務中主要使用的仍是L2損失函數，L2損失的好處有不少，例如能夠直接提升PSNR等指標。可是L2指標與人類感知的圖像質量相關性較差，例如其假設噪聲與圖像的局部區域無關。在有些狀況下，L1損失函數得到的圖像
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相關文章
- 1. 閱讀論文《Loss Functions for Image Restoration With Neural Networks》
- 2. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》論文閱讀筆記
- 3. [論文閱讀]Bag of Tricks for Image Classification with Convolutional Neural Networks
- 4. 【論文閱讀】Bag of Tricks for Image Classification with Convolutional Neural Networks
- 5. 論文閱讀：Bag of Tricks for Image Classification with Convolutional Neural Networks
- 6. 《Pyramid Attention Networks for Image Restoration》閱讀筆記
- 7. 【深度學習】Loss Functions for Neural Networks for Image Processing
- 8. 論文閱讀筆記之——《Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks》
- 9. 論文閱讀：Deep Neural Networks for Object Detection
- 10. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》