Recent advancements in light field super-resolution (SR) have yielded
impressive results. In practice, however, many existing methods are limited by
assuming fixed degradation models, such as bicubic downsampling, which hinders
their robustness in real-world scenarios with complex degradations. To address
this limitation, we present LF-DEST, an effective blind Light Field SR method
that incorporates explicit Degradation Estimation to handle various degradation
types. LF-DEST consists of two primary components: degradation estimation and
light field restoration. The former concurrently estimates blur kernels and
noise maps from low-resolution degraded light fields, while the latter
generates super-resolved light fields based on the estimated degradations.
Notably, we introduce a modulated and selective fusion module that
intelligently combines degradation representations with image information,
allowing for effective handling of diverse degradation types. We conduct
extensive experiments on benchmark datasets, demonstrating that LF-DEST
achieves superior performance across a variety of degradation scenarios in
light field SR.

最近光场超分辨率（SR）的最新进展取得了令人印象深刻的结果。然而，在实践中，许多现有方法由于假设了固定的降级模型（如双三次降采样），这限制了它们在具有复杂退化的现实场景中的鲁棒性。为了解决这个问题，我们提出了 LF-DEST，这是一种有效的盲光场超分辨率方法，它结合了明确的退化估计来处理各种降级类型。LF-DEST 包括两个主要组成部分：退化估计和光场恢复。前者从低分辨率退化的光场中同时估计模糊核和噪声图，而后者根据估计的退化生成超分辨率光场。值得注意的是，我们引入了一个调制和选择性融合模块，智能地将退化表示与图像信息结合起来，以便有效处理各种降级类型。我们在基准数据集上进行了广泛的实验证明，LF-DEST 在光场 SR 的各种降级场景中实现了卓越的性能。