论文标题
结构化和局部图像恢复
Structured and Localized Image Restoration
论文作者
论文摘要
我们提出了一种新颖的图像恢复方法，该方法利用局部结构化预测和非线性多任务学习来利用思想。我们优化了一个惩罚的能量函数，该函数通过测量要恢复的补丁和清洁贴片之间的距离的术语和从事先收集的外部数据库中进行清洁的距离进行了正规化。最终的估计器具有强大的统计保证，可利用重叠贴片的局部依赖性属性。我们基于均方和欧几里得规范误差得出相应的能量算法。最后，我们使用标准基准证明了模型对不同图像恢复问题的实际有效性。
We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning. We optimize a penalized energy function regularized by a sum of terms measuring the distance between patches to be restored and clean patches from an external database gathered beforehand. The resulting estimator comes with strong statistical guarantees leveraging local dependency properties of overlapping patches. We derive the corresponding algorithms for energies based on the mean-squared and Euclidean norm errors. Finally, we demonstrate the practical effectiveness of our model on different image restoration problems using standard benchmarks.
