Light-field view synthesis using convolutional block attention module

TL;DR

This paper introduces a novel deep learning approach with attention mechanisms to synthesize high-quality light-field views from sparse input views, improving resolution and view synthesis accuracy.

Contribution

The paper proposes a new multi-stage neural network with convolutional block attention modules for light-field view synthesis, outperforming existing methods.

Findings

Outperforms state-of-the-art methods by 0.5 dB on real datasets

Uses attention modules for better feature focus and image refinement

Demonstrates robustness through extensive experiments and ablation studies

Abstract

Consumer light-field (LF) cameras suffer from a low or limited resolution because of the angular-spatial trade-off. To alleviate this drawback, we propose a novel learning-based approach utilizing attention mechanism to synthesize novel views of a light-field image using a sparse set of input views (i.e., 4 corner views) from a camera array. In the proposed method, we divide the process into three stages, stereo-feature extraction, disparity estimation, and final image refinement. We use three sequential convolutional neural networks for each stage. A residual convolutional block attention module (CBAM) is employed for final adaptive image refinement. Attention modules are helpful in learning and focusing more on the important features of the image and are thus sequentially applied in the channel and spatial dimensions. Experimental results show the robustness of the proposed method. Our proposed network outperforms the state-of-the-art learning-based light-field view synthesis methods on two challenging real-world datasets by 0.5 dB on average. Furthermore, we provide an ablation study to substantiate our findings.