Incorporating degradation estimation in light field spatial super-resolution

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 L ight F ield SR method that incorporates explicit D egradation Est imation 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, effectively handling diverse degradation types. We conduct extensive experiments on benchmark datasets, demonstrating that LF-DEST achieves superior performance across various degradation scenarios in light field SR. The implementation code is available at https://github.com/zeyuxiao1997/LF-DEST . • Advanced Degradation Estimation : LF-DEST estimates blur kernels and noise maps, surpassing baselines. • Modulated and Selective Fusion Module (MSF) : MSF fuses degradations with features, ensuring superior detail recovery. • Unified Framework for Future Work : LF-DEST models multiple degradations, setting new benchmarks.