LFSRDiff: Light Field Image Super-Resolution via Diffusion Models
Wentao Chao Fuqing Duan Xuechun Wang Yingqian Wang Guanghui Wang
Light field (LF) image super-resolution (SR) is a challenging problem due to
its inherent ill-posed nature, where a single low-resolution (LR) input LF
image can correspond to multiple potential super-resolved outcomes. Despite
this complexity, mainstream LF image SR methods typically adopt a deterministic
approach, generating only a single output supervised by pixel-wise loss
functions. This tendency often results in blurry and unrealistic results.
Although diffusion models can capture the distribution of potential SR results
by iteratively predicting Gaussian noise during the denoising process, they are
primarily designed for general images and struggle to effectively handle the
unique characteristics and information present in LF images. To address these
limitations, we introduce LFSRDiff, the first diffusion-based LF image SR
model, by incorporating the LF disentanglement mechanism. Our novel
contribution includes the introduction of a disentangled U-Net for diffusion
models, enabling more effective extraction and fusion of both spatial and
angular information within LF images. Through comprehensive experimental
evaluations and comparisons with the state-of-the-art LF image SR methods, the
proposed approach consistently produces diverse and realistic SR results. It
achieves the highest perceptual metric in terms of LPIPS. It also demonstrates
the ability to effectively control the trade-off between perception and
distortion. The code is available at
\url{https://github.com/chaowentao/LFSRDiff}.