Most deep learning-based image SR algorithms do not apply the down-sampling to the reconstructed process. Given this fact and inspired by the iteration idea, we propose a novel image SR method based on the down-sampling iterative module and deep CNN, which explores a new basic iterative module combining up- and down-sampling processes. Each iteration of the iterative module generates the intermediate LR prediction and the HR image. The final reconstructed result is obtained by the weighted summation of the intermediate predicted images generated by multiple iterations. During the training, we adopt the adaptive loss function to achieve fast convergence and accurate reconstruction. Detailed experimental comparisons and analyses show that our method is superior to some state-of-the-art methods in objective performance evaluation and visual effects.

中文翻译：

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基于下采样迭代模块和深度CNN的图像超分辨率

大多数基于深度学习的图像 SR 算法不会将下采样应用于重建过程。鉴于这一事实并受迭代思想的启发，我们提出了一种基于下采样迭代模块和深度 CNN 的新型图像 SR 方法，该方法探索了一种新的结合上采样和下采样过程的基本迭代模块。迭代模块的每次迭代都会生成中间 LR 预测和 HR 图像。通过多次迭代生成的中间预测图像的加权求和得到最终的重构结果。在训练过程中，我们采用自适应损失函数来实现快速收敛和准确重建。详细的实验比较和分析表明，我们的方法在客观性能评估和视觉效果方面优于一些最先进的方法。