2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-MRII.1
Paper Title LIGHT-FIELD VIEW SYNTHESIS USING A CONVOLUTIONAL BLOCK ATTENTION MODULE
Authors Muhammad Shahzeb Khan Gul, Fraunhofer Institute for Integrated Circuits IIS, Germany; Muhammad Umair Mukati, Technical University of Denmark, Denmark; Michel Batz, Fraunhofer Institute for Integrated Circuits IIS, Germany; Søren Forchhammer, Technical University of Denmark, Denmark; Joachim Keinert, Fraunhofer Institute for Integrated Circuits IIS, Germany
SessionSS-MRII: Special Session: Models and representations for Immersive Imaging
LocationArea A
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Special Sessions: Models and Representations for Immersive Imaging
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.