## 3 The Proposed Method
### Refinement Module  
To refine the image, the refine module starts with a \(3\times 3\) convolution, which cuts down the number of channels to 3. These channels are then fed directly to a single stage of the HINet network. The single-stage of the HINet as illustrate in Figure 4, is a U-Net-shaped architecture that employs an encoder-decoder designed to enhance feature extraction and image reconstruction. On the encoder side, a downsampling is carried out with a convolution of kernel size 4. Half-Instance Normalization Blocks (HIN Blocks) are used to process features while downsampling the channels twice.  
The HIN Block is essential for enhancing feature representation while maintaining efficiency. The input features \(F_{\text{in}}\in\mathbb{R}^{\mathbb{C}_{\text{in}}\times H\times W}\) are processed and generate intermediate features \(F_{\text{mid}}\in\mathbb{R}^{\mathbb{C}_{\text{out}}\times H\times W}\), using a \(3\times 3\) convolutional layer. Intermediate features \(F_{\text{mid}}\) are split into two equal parts: \(F_{\text{mid}1}\) and \(F_{\text{mid}2}\), each with \(\mathbb{C}_{\text{out}}/2\) channels. \(F_{\text{mid}1}\) undergoes instance normalization (IN) with learnable affine parameters. \(F_{\text{mid}2}\) remains unchanged by passing it through identity to retain contextual information. The concatenation of the two portions occurs over the channel dimension. The residual features \(R_{\text{out}}\) are obtained by processing the concatenated features via a \(3\times 3\) convolutional layer sandwiched between two leaky ReLU activations (with a leaky factor of 0.2). Taking the supplied features as input \(F_{\text{in}}\) for the convolutional layer of size 1, a shortcut link is made to them. By adding the shortcut features to the residual features \(R_{\text{out}}\), the final output \(F_{\text{out}}\) is produced.  
An upsampling operation was accomplished on the decoder side by using a kernel size of 2 in a transposed convolutional layer. To minimize information loss during resampling,  
Figure 4: The structure of the Half-Instance Normalization Layer.  
features are refined using a single residual block [62]. The encoder's features are then combined with the upsampled features. After being processed via a 3 \(\times\) 3 convolutional layer, the input features \(F_{\text{in}}\) with dimensions \(\mathbb{R}^{\text{C}_{\text{in}}\times H\times W}\) are fed into the ResBlock via a leaky ReLU activation function with a leaky factor of 0.2. Introducing non-linearity enables the network to learn more intricate patterns. A second leaky ReLU activation is applied after the features flow through a third 3 \(\times\) 3 convolutional layer. By collecting increasingly complex details and patterns in the incoming data, this further refines the features. To create a residual connection, the given features are used as input \(F_{\text{in}}\) for a convolutional layer of size 1, and the processed features from the second activation are summed to create the final output \(F_{\text{out}}\) of the ResBlock. The final occlusion-free light-field image is obtained by feeding the result of the refinement module through a Tanh activation function.