This is the FDL toolbox providing matlab functions for Light Fields processing in the Fourier Disparity Layer representation (see the project page).

The main features are listed here (detailed documentations of the referenced functions are available in the matlab (.m) files):

• fftImgs and ifftImgs (both cpu and gpu):

  • Functions for 2D FFT and inverse FFT for a stack of images.
  • Options for signal extension (padding and windowing of padded borders).
  • Supports hexagonal sampling of the input images (e.g. RAW Lytro data).

• refocusFFT (both cpu and gpu):

  • Fourier domain implementation of the "Shift and sum" light field refocusing algorithm.

• CalibrateFDL_UVD_cpu and CalibrateFDL_UVD_gpu:

  • Gradient descent based calibration to determine view positions on the camera plane (U, V) and disparity values (D) of the layers (see algorithm description in the TIP 2019 paper [1]).
  • Support for independent U,V,D parameters per color channel (e.g. for chromatic aberration estimation).
  • Options for estimating only D knowing U,V, or only U,V knowing D.
  • Options for improved robustness (e.g. see 'UseSignFlip', 'numIterLowFreqs' options in the function documentation).

• CalibrateFDL_NoR1_cpu and CalibrateFDL_NoR1_gpu:

  • Calibration with a relaxed version of the FDL model with no Rank-1 constraint on the parameter matrix containing the shift of each layer to reconstruct each view (see details in [1]).
  • FDL constructed from this relaxed model are only suitable to reconstruct images at the same view positions as the input views (e.g. for denoising or spatial super-resolution).
  • It is recommended to initialise the matrices of parameters using the calibration results of the rank 1 constrained calibration (CalibrateFDL_UVD_xxx functions).

• ComputeFDL_cpu and ComputeFDL_gpu:

  • Simple FDL construction from the TIP 2019 paper [1].
  • Use l2 and 2nd order view regularisation.

• ComputeFDL_SparseReg_cpu (cpu only):

  • Other version of the FDL contruction.
  • Use l1 and l2 regularisation.

• ComputeFDL_SuperRes_gpu (gpu only):

  • Super-resolution algorithm from the ICCP 2020 paper [2].
  • Supports hexagonal input sampling (e.g. RAW Lytro data).
  • Supports color regularisation to reduce color noise/artifacts (see details in [2]).
  • Supports spatial and angular preconditioning (see details in [2] for spatial pre-conditioning).
  • This method can also be used for FDL construction without super-resolution (super-resolution factor = 1).

• FDL_Complete_SuperRes (gpu only):

  • Completion algorithm from the ICCP 2020 paper [2]:
  • Supports all the tools from the super-resolution algorithm (e.g. completion jointly with super-resolution and color regularisation for demosaicing).

• RenderModel (both cpu and gpu)

  • Class for fast light field rendering from a FDL model.
  • Control of viewpoint, focus, aperture shape, aperture size.
  • Possibility to save the FDL model (using mat file system).

• RenderAppMain (both cpu and gpu)

  • GUI application for FDL rendering based on the RenderModel class.
  • User interation for controlling viewpoint, focus, aperture shape and aperture size.
  • Automatic refocusing on click using a disparity map (if not available, a fast disparity estimation can be made).
  • Visualisation of the Fourier magnitude spectrum of the rendered result.
  • Possibility to save the FDL model (using mat file system).

• Encoder/Decoder for FDL models using a tree structure as described in the paper: M. Le Pendu, C. Ozcinar and A. Smolic, "Hierarchical Fourier Disparity Layer Transmission for Light Field Streaming," ICIP 2020.
  • See documentation in ./FDLTree_Codec.README.md.

• Demo_FDL_Simple (both cpu and gpu)

  • Demo script showing an example usage of the FDL processing chain (method without super-resolution from [1]):
  • Loads views, apply Fourier Transform, FDL calibration, FDL construction and launch Rendering GUI.

• Demo_FDL_SR (gpu only)

  • Similar to Demo_FDL_Simple but using the FDL construction method with super-resolution from [2].

• Demo_FDL_View_Extract (gpu only)

  • Script for Lytro camera view extraction using FDL completion, super-resolution and color demosaicing in [2]. The V-Sense Light Field Toolbox is required to previously convert RAW sensor data into incomplete views along their masks indicating the missing pixels.

• Demo_FDLTree_Codec (both cpu and gpu)

  • Demo script of the FDL Tree codec. The demo generates a FDL with the Demo_FDL_Simple script, encodes/decodes it in the FDL Tree representation, and displays decoded results for different levels of the tree.

The toolbox has been developped using Matlab 2017a and Windows 10.

For the GPU versions of the tools, Matlab's Parallel Processing Toolbox and a CUDA GPU are required.

When using the FDL toolbox in your reasearch, please cite the relevant papers:

[1] M. Le Pendu, C. Guillemot and A. Smolic, "A Fourier Disparity Layer Representation for Light Fields," in IEEE Transactions on Image Processing, vol. 28, no. 11, pp. 5740-5753, Nov. 2019.

[2] M. Le Pendu, A. Smolic "High Resolution Light Field Recovery with Fourier Disparity Layer Completion, Demosaicing, and Super-Resolution", International Conference on Computational Photography (ICCP) 2020.