  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.
