Incorporating Degradation Estimation in Light Field Spatial Super-Resolution
This work addresses robustness issues in light field super-resolution for real-world applications, representing an incremental improvement over methods with fixed degradation assumptions.
The paper tackles the problem of light field super-resolution under complex real-world degradations by proposing LF-DEST, a blind method that incorporates explicit degradation estimation, achieving superior performance across various degradation scenarios.
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.