Compressive 4D Light Field Reconstruction Using Orthogonal Frequency Selection

Fatma Hawary (1, 2) , Guillaume Boisson (2) , Christine Guillemot (1) , Philippe Guillotel (2)

1 Sirocco - Analysis representation, compression and communication of visual data
2 Technicolor R & I [Cesson Sévigné]

We present a new method for reconstructing a 4D light field from a random set of measurements. A 4D light field block can be represented by a sparse model in the Fourier domain. As such, the proposed algorithm reconstructs the light field, block by block, by selecting frequencies of the model that best fits the available samples, while enforcing orthogonality with the approximation residue. The method achieves a very high reconstruction quality, in terms of Peak Signal-to-Noise Ratio (PSNR). Experiments on several datasets show significant quality improvements of more than 1dB compared to state-of-the-art algorithms.

Mots clés: Sparsity; Light field; Non-regular sampling; Reconstruction

Domaines: Traitement du signal et de l'image [eess.SP]

Fatma Hawary, Guillaume Boisson, Christine Guillemot, Philippe Guillotel. Compressive 4D Light Field Reconstruction Using Orthogonal Frequency Selection. ICIP 2018 - 25th IEEE International Conference on Image Processing, Oct 2018, Athens, Greece. pp.3863-3867, ⟨10.1109/ICIP.2018.8451110⟩. ⟨hal-01810353⟩