Incorporating Degradation Estimation in Light Field Spatial Super-Resolution

TL;DR

LF-DEST is a novel light field super-resolution method that explicitly estimates degradations like blur and noise to improve robustness in real-world scenarios with complex degradations.

Contribution

The paper introduces LF-DEST, a blind super-resolution approach that jointly estimates degradations and restores light fields, enhancing performance under diverse degradation conditions.

Findings

Outperforms existing methods on benchmark datasets.

Effectively handles various complex degradations.

Demonstrates robustness in real-world scenarios.

Abstract

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.