Spatial-angular separable convolution (SAS-conv) has been widely used for efficient and effective 4D light field (LF) feature embedding in different tasks, which mimics a 4D convolution by alternatively operating on 2D spatial slices and 2D angular slices. In this paper, we argue that, despite its global intensity modeling capabilities, SAS-conv can only embed local geometry information into the features, resulting in inferior performances in the regions with textures and occlusions. Because the epipolar lines are highly related to the scene depth, we introduce the concept of spatial-angular correlated convolution (SAC-conv). By alternating 2D convolutions on the vertical and horizontal epipolar slices, SAC-conv can embed global and robust geometry information into the features. We verify that SAS-conv and SAC-conv are skilled at different aspects of 4D LF feature embedding through a detailed feature and error analysis. Based on their complementarity, we further combine SAS-conv and SAC-conv by a parallel residual connection, forming a new spatial-angular versatile convolution (SAV-conv) module. We conduct comprehensive experiments on two representative LF reconstruction tasks, i.e., LF angular super-resolution and LF spatial super-resolution. Both the quantitative and qualitative results demonstrate that, without any extra parameters, networks upgraded with our proposed SAV-conv notably outperform those upgraded with SAS-conv and achieve a new state-of-the-art performance. Codes are available at https://github.com/Joechann0831/SAV_conv .

Light field images taken by plenoptic cameras often have a trade-off between spatial and angular resolutions. In this paper, we propose a novel spatial super-resolution approach for light field images by jointly exploiting internal and external similarities. The internal similarity refers to the correlations across the angular dimensions of the 4D light field itself, while the external similarity refers to the cross-scale correlations learned from an external light field dataset. Specifically, we advance the classic projection-based method that exploits the internal similarity by introducing the intensity consistency checking criterion and a back-projection refinement, while the external correlation is learned by a CNN-based method which aggregates all warped high-resolution sub-aperture images upsampled from the low-resolution input using a single image super-resolution method. By analyzing the error distributions of the above two methods and investigating the upperbound of combining them, we find that the internal and external similarities are complementary to each other. Accordingly, we further propose a pixel-wise adaptive fusion network to take advantage of both their merits by learning a weighting matrix. Experimental results on both synthetic and real-world light field datasets validate the superior performance of the proposed approach over the state-of-the-arts.

Light field (LF) cameras suffer from a fundamental trade-off between spatial and angular resolutions. Additionally, due to the significant amount of data that needs to be recorded, the Lytro ILLUM, a modern LF camera, can only capture three frames per second. In this paper, we consider space-time super-resolution (SR) for LF videos, aiming at generating high-resolution and high-frame-rate LF videos from low-resolution and low-frame-rate observations. Extending existing space-time video SR methods to this task directly will meet two key challenges: (1) how to re-organize sub-aperture images (SAIs) efficiently and effectively given highly redundant LF videos, and (2) how to aggregate complementary information between multiple SAIs and frames considering the coherence in LF videos. To address the above challenges, we propose a novel framework for space-time super-resolving LF videos for the first time. First, we propose a novel Multi-Scale Dilated SAI Re-organization strategy for re-organizing SAIs into auxiliary view stacks with decreasing resolution as the Chebyshev distance in the angular dimension increases. In particular, the auxiliary view stack with original resolution preserves essential visual details, while the down-scaled view stacks capture long-range contextual information. Second, we propose the Multi-Scale Aggregated Feature extractor and the Angular-Assisted Feature Interpolation module to utilize and aggregate information from the spatial, angular, and temporal dimensions in LF videos. The former aggregates similar contents from different SAIs and frames for subsequent reconstruction in a disparity-free manner at the feature level, whereas the latter interpolates intermediate frames temporally by implicitly aggregating geometric information. Compared to other potential approaches, experimental results demonstrate that the reconstructed LF videos generated by our framework achieve higher reconstruction quality and better preserve the LF parallax structure and temporal consistency.

The widespread usage of high-definition screens on edge devices stimulates a strong demand for efficient image restoration algorithms. The way of caching deep learning models in a look-up table (LUT) is recently introduced to respond to this demand. However, the size of a single LUT grows exponentially with the increase of its indexing capacity, which restricts its receptive field and thus the performance. To overcome this intrinsic limitation of the single-LUT solution, we propose a universal method to construct multiple LUTs like a neural network, termed MuLUT. First, we devise novel complementary indexing patterns, as well as a general implementation for arbitrary patterns, to construct multiple LUTs in parallel. Second, we propose a re-indexing mechanism to enable hierarchical indexing between cascaded LUTs. Finally, we introduce channel indexing to allow cross-channel interaction, enabling LUTs to process color channels jointly. In these principled ways, the total size of MuLUT is linear to its indexing capacity, yielding a practical solution to obtain superior performance with the enlarged receptive field. We examine the advantage of MuLUT on various image restoration tasks, including super-resolution, demosaicing, denoising, and deblocking. MuLUT achieves a significant improvement over the single-LUT solution, e.g., up to 1.1 dB PSNR for super-resolution and up to 2.8 dB PSNR for grayscale denoising, while preserving its efficiency, which is 100× less in energy cost compared with lightweight deep neural networks.

Non-local self-similarity has been well exploited in the single image super-resolution task as an effective prior. However, due to the difficulty of modeling the 4D correspondence globally, the potential of the non-local prior is less revealed for light field (LF) super-resolution. Meanwhile, existing non-local models only utilize the global spatial correspondence, but largely neglect the global geometric correspondence. To address the aforementioned problems, we propose a Decoupled Selective Matching Network (DSMNet) for LF super-resolution, by designing a novel selective matching mechanism to flexibly extract non-local information from specific 4D positions in an LF. Such a mechanism matches the reference patch with several auxiliary patches dynamically searched from predefined windows, which promotes efficiency while improving performance compared to the existing non-local models. Specifically, our DSMNet decouples the whole LF into Sub-Aperture Images (SAIs) and Epipolar Plane Images (EPIs). For each SAI patch, we separately perform the selective matching inside the current SAI and cross different SAIs to exploit the global spatial correspondence efficiently. For each EPI patch, we separately perform the selective matching in EPIs of different orientations to embed robust LF geometric information into features by enhancing EPI textures, which exploits the global geometric correspondence in an efficient manner. Comprehensive experiments validate that DSMNet outperforms state-of-the-art LF super-resolution methods both quantitatively and qualitatively.