MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution

TL;DR

MaskBlur introduces a novel data augmentation technique for light field image super-resolution that simultaneously manipulates spatial and angular data, significantly improving model performance across multiple LF image tasks.

Contribution

The paper proposes MaskBlur, a new spatial and angular data augmentation strategy specifically designed for light field images, addressing a gap in existing augmentation methods.

Findings

Enhances super-resolution performance of existing methods.

Effective for LF denoising, deblurring, and low-light enhancement.

Code is publicly available for reproducibility.

Abstract

Data augmentation (DA) is an effective approach for enhancing model performance with limited data, such as light field (LF) image super-resolution (SR). LF images inherently possess rich spatial and angular information. Nonetheless, there is a scarcity of DA methodologies explicitly tailored for LF images, and existing works tend to concentrate solely on either the spatial or angular domain. This paper proposes a novel spatial and angular DA strategy named MaskBlur for LF image SR by concurrently addressing spatial and angular aspects. MaskBlur consists of spatial blur and angular dropout two components. Spatial blur is governed by a spatial mask, which controls where pixels are blurred, i.e., pasting pixels between the low-resolution and high-resolution domains. The angular mask is responsible for angular dropout, i.e., selecting which views to perform the spatial blur operation. By doing so, MaskBlur enables the model to treat pixels differently in the spatial and angular domains when super-resolving LF images rather than blindly treating all pixels equally. Extensive experiments demonstrate the efficacy of MaskBlur in significantly enhancing the performance of existing SR methods. We further extend MaskBlur to other LF image tasks such as denoising, deblurring, low-light enhancement, and real-world SR. Code is publicly available at \url{https://github.com/chaowentao/MaskBlur}.