## 3 Computational Modeling of the Retina
Each pixel is nonlinearly combined with the surrounding image by defining different pixel interactions at different spatial scales. (2) Multi-scale, where the resulting pixel/surrounding interactions are weighted and summed to obtain the equalized output image. The assignment of the weights is based on a set of rules regarding undersized or oversized surroundings [92]. [PERSON] and [PERSON] developed a TMO motivated by the driveline system adaptation set up in photoreceptors. This model depicts the automatic brightness arrangement that colored photoreceptors have. The kernel of their algorithms is the evolution of a photoreceptor model and their TMO is based on the overall image adaptation by adjusting the Naka-Rushton computer address in a comprehensive manner (using the average luminousness of the scene) or a specific form (using the average luminousness of the color). This arrangement lacks luminousness coherence over time due to its static formulation, i.e., each piece of the framework is processed individually [92]. [PERSON] et al. [96; 97] and [PERSON] [98] proposed a front-end retina-like model for computer vision tasks and applied it to tone-mapping images and videos. They modeled the retina in adaptation layers, i.e., the first layer is linked to photoreceptor adaptation and the other is related to ganglion cells. In the middle of these layers, there is a permanent spatial-temporal filter that models the synaptic OPL, which allows for communication between photoreceptors and horizontal retinal cells. The authors present a non-linear specific process where the photoreceptor action mode depends on the luminousness received within a nearby region. The OPL layers were thus employed as spectral lightening and contrast upgrading. In the same way, the ganglion cell layer again uses one non-linear mechanism for photoreceptor adaptation with different parameters. To calculate the average luminosity, the authors considered a small region close to each pixel  
Color processing is done using a see-through layer color sampling multiplexing/demultiplexing network (commonly found in cameras). They use the temporal characteristic of the OPL filter to implement a temporal consistency throughout the color sequence [92]. [PERSON] et al. directly connected the OPL output-to-input ratio with the intended non-linearity in the ganglion cell layer and the virtual retina (VR), where the OPL enters a contrast gain control platform before the ganglion cell layer [96]. [PERSON] et al. introduced a method for dehazing static images [99]. This method relies on a complete retinal model, i.e., from photoreceptors to retinal ganglion cells. Photoreceptors, bipolar, amacrine, and ganglion cells were modeled as a cascade of Gaussian signals that is a difference of Gaussian filters, which represents the complex associations between colors and the full or specific adaptation. [PERSON] et al. introduced a mathematical model based on biophysics, where they reproduced the rupture activity of the SACs, proposing a plausible, generic, and robust mechanism for its production [20]. Based on the bifurcation analysis, they provide some biophysical parameters for regulating calcium and potassium activity through burst control. All models known in the literature describe wave activity, though so far, no models describe the dynamics of individual SACs. In these models, there is no description of the biophysical mechanisms of SAC bursting activity or the role played by biophysical parameters since the models were based on the \"simple\" description of cellular activity by using a small number of adjustable parameters [20]. The authors based their model on the model proposed by [PERSON] et al., where the biophysical analysis of slow after hyperpolarization (sAHP) dynamics leads to equations and parameter values present in their model [20; 100]. [PERSON] et al. proposed a method where they synthesize ocular background images directly from the data [37]. Using the vessel segmentation technique, they combined true images of the fundus with their vessel trees.