## 4 Experiments  
### Experimental Setup  
Using the methodology outlined in [58], we trained and tested our network as follows:  
**Training dataset:** We train our LF occlusion removal network using a dataset that combines real-world occlusion scenarios with synthetic occlusion generation in a manner identical to the mask embedding method indicated by [54]. This method establishes occluded LF images by embedding occlusion masks into occlusion-free LF images, thereby simulating a range of occlusion conditions. A variety of disparity scenarios are represented in the LF images by the random placement of one to three occlusion masks during the mask-embedding procedure. In addition to the 80 mask images used in the [54] approach, 21 more thick and large real occlusion images were added to enhance the training dataset, tackling the challenge of removing large occlusions. We make sure that our LF images only include objects with negative disparity in order to provide ground-truth occlusion-free images.We used 1418 light-field images (out of 2957 total) from the DUTLF-V2 dataset [63], a dense LF dataset taken with the Lytro Illum camera [64], for this purpose. Our model is able to learn and generalize occlusion elimination over a wide range of real-world scenarios because of the precise selection and augmentation of the training data.  
**Testing dataset:** We use four synthetic sparse LF scenes (4-Syn) and nine synthetic sparse LF scenes (9-Syn), which were created by [54; 59], respectively, to assess our network's performance on sparse light fields (LFs). Because Stanford CD scene [65] is a real sparse LF image with ground truth accessible, we include it in our quantitative comparison. From the DUTLF-V2 test dataset [63], we chose 615 LFs for dense LFs and an extra 33 real occlusion images. For the purpose of evaluating multi-disparity occlusion scenarios, which we refer to as Single Occ and Double Occ, respectively, we employ a mask-embedding technique with a disparity range of [1; 4]. We employ several real-world sparse and dense occlusion LF scenes that are publicly available for qualitative comparison. A thorough assessment of our method's performance in sparse LF environments is given by the sparse LF dataset, which includes scenes taken by [59] and the Stanford CD scene [65]. The Stanford Lytro dataset [66] and EPFL-10 [67], both taken with the Lytro Illum camera, form the dense LF dataset. Our network's ability to handle intricate occlusions in dense LF environments is thoroughly evaluated because of the variety of occlusion scenarios and disparity levels provided by these datasets.  
**Training details**: An angular and spatial resolution of (U \(\times\) V \(\times\) X \(\times\) Y) = (\(\theta\)\(\times\) 9 \(\times\) 600 \(\times\) 400) is present in the LF images in the DUTLF-V2 dataset. The 300 \(\times\) 200 spatial resolution is applied to the central 5 \(\times\) 5 pictures for our needs. To achieve a resolution of (256 \(\times\) 192), we arbitrarily center-crop and flip images horizontally during training. To implement occlusion embedding, we employ the mask-embedding technique to randomly pick, mix, and shuffle one or more masks in RGB images. We optimize our model using the ADAM optimizer with \((\beta_{1},\beta_{2})=(0.5,0.9)\) and a batch size of 4. Because of the restricted GPU memory, the \(\lambda_{1}\) and \(\lambda_{2}\) parameter values are set to 0.01 and 120, respectively. A learning rate of 0.001 is used initially and is halved every 150 epochs. Using the PyTorch framework, 500 epochs of training are completed in 20 h on a single 4090 Nvidia Geforce GPU.