In this report we summarize the 2nd NTIRE challenge on light field (LF) image super-
resolution (SR) with a focus on new methods and results. This challenge aims at super …

In the natural evolutionary process, biological creatures have developed diverse visual
structures apt for their habitational environments. These natural vision structures have …

In this report, we summarize the first NTIRE challenge on light field (LF) image super-
resolution (SR), which aims at super-resolving LF images under the standard bicubic …

Compared with external-supervised learning-based (ESLB) methods, self-supervised
learning-based (SSLB) methods can overcome the domain gap problem caused by different …

Compared to 2D imaging data, the 4D light field (LF) data retains richer scene's structure
information, which can significantly improve the computer's perception capability, including …

Light Field (LF) cameras capture rich information in 4D LF images by recording both
intensity and angular directions, making it crucial to learn the inherent spatial-angular …

Light field (LF) depth estimation plays a crucial role in many LF-based applications. Existing
LF depth estimation methods consider depth estimation as a regression problem, where a …

The performance of light field (LF) image super-resolution (SR) has been significantly
improved with the development of deep learning techniques. In recent state-of-the-art …

Recent advancements in light field super-resolution (SR) have yielded impressive results. In
practice, however, many existing methods are limited by assuming fixed degradation …

Transformer-based methods have demonstrated impressive performance in 4D light field
(LF) super-resolution by effectively modeling long-range spatial-angular correlations, but …