Real-world image super-resolution (RWSR) is a long-standing problem as
low-quality (LQ) images often have complex and unidentified degradations.
Existing methods such as Generative Adversarial Networks (GANs) or continuous
diffusion models present their own issues including GANs being difficult to
train while continuous diffusion models requiring numerous inference steps. In
this paper, we propose an Iterative Token Evaluation and Refinement (ITER)
framework for RWSR, which utilizes a discrete diffusion model operating in the
discrete token representation space, i.e., indexes of features extracted from a
VQGAN codebook pre-trained with high-quality (HQ) images. We show that ITER is
easier to train than GANs and more efficient than continuous diffusion models.
Specifically, we divide RWSR into two sub-tasks, i.e., distortion removal and
texture generation. Distortion removal involves simple HQ token prediction with
LQ images, while texture generation uses a discrete diffusion model to
iteratively refine the distortion removal output with a token refinement
network. In particular, we propose to include a token evaluation network in the
discrete diffusion process. It learns to evaluate which tokens are good
restorations and helps to improve the iterative refinement results. Moreover,
the evaluation network can first check status of the distortion removal output
and then adaptively select total refinement steps needed, thereby maintaining a
good balance between distortion removal and texture generation. Extensive
experimental results show that ITER is easy to train and performs well within
just 8 iterative steps. Our codes will be available publicly.

我们提出了一个迭代令牌评估和改进 (ITER) 框架，用于实现实际场景图像超分辨率 (RWSR)，该框架利用离散扩散模型在离散令牌表示空间中运行，即利用提前用高质量图像进行预训练的 VQGAN 编码书中的特征索引。我们将 RWSR 分为两个子任务：去除失真和生成纹理。去除失真涉及对低质量图像进行简单的高质量令牌预测，而生成纹理则使用离散扩散模型通过令牌改进网络对去除失真结果进行迭代改进。我们还提出在离散扩散过程中包含一个令牌评估网络，它学习评估哪些令牌是良好的恢复，并帮助改进迭代改进的结果。此外，评估网络可以首先检查去除失真的状态，然后自适应选择所需的总改进步骤，从而在去除失真和生成纹理之间保持良好的平衡。广泛的实验结果表明，ITER 易于训练，在只进行 8 个迭代步骤的情况下表现良好。我们的代码将公开提供。