## 1 Introduction  
Nowadays, light-emitting diodes (LEDs) are widely used as common lighting sources because of their numerous advantages, such as excellent visibility, durability, and low power consumption. Moreover, the ability to switch the light intensity fast [1; 2] gives LEDs the abilities to transmit high-speed data, provided that the switching rate, or frequency, is higher than 200 Hz for human eyes' safety [3]. On the receiver side, two types of light receivers can be used, which classifies the LED-based communication into two research directions [4]. OWC/OCC utilized cameras to receive modulated light, and photodiodes have been widely employed in the VLC system. Although OCC has a lower data rate compared to VLC since it receives data via an image sensor [4; 5], OCC is still preferable in applications where mobility is crucial because of the wider field of view (FoV) of a camera compared to a photodiode, for example, indoor localization using a personal smartphone [6; 7] or vehicular OCC system [8; 9; 10]. On the basis of the point of view of OCC technologies, internet of vehicles (IoV) is one of the most promising areas where these technologies could be deployed. Compared to using the traditional radio frequency (RF) communication, OWC/OCC and VLC are considered as reliable, safe for human health, and low-cost communication technologies [4; 11]. Regarding classification into sub-areas, IoV can be divided into two communication fields: Vehicles-to-vehicles (V2V) and vehicle-to-infrastructure (V2X). In either sub-area, the vehicles can either act as transmitters or receivers in the OCC system. For transmitting optical data, there are light sources that are designed at both the front and back sides of vehicles. For receiving data, there are also various kinds of cameras that could be attached to vehicles. Despite the enormous potential of the OWC/OCC vehicular system for a lucrative industrial market, the development of these new technologies still suffers several challenges, as has been pointed out in [12]. However, sources of white, noise such as ambient light radiation from the sun and streetlight, are one of the main factors causing the degradation of OWC/OCC vehicular systems. Many studies have been carried out to demonstrate the feasibility of OWC/OCC technology in V2V/V2X systems (see, for example, [8; 9; 10]), and mostly, the performance is investigated with respect to Gaussian white noise with different signal-to-noise ratio (SNR) values. Another type of distortion that can reduce the performance of a vehicular OWC/OCC system is blurred phenomenon, which generally occurs in any system related to camera and image processing. In an OWC/OCC vehicular scenario, vehicle vibration and mobility, weather conditions (rainy, foggy, and snowy, to mention a few), and camera focusing are the dominant candidates that can cause the blurring of received images. However, there are various coding techniques for decoding and error correction. Eventually, these techniques are developed to perform well in RF communication systems. Hence, to deal with white noise caused by ambient light in OCC vehicular systems, these techniques could also yield similar performance. However, the decoding accuracy of an OCC vehicular system can be extremely low when the blur effect occurs because the blurring process can sum up and fairly redistribute all intensity values using a point-spread function [13], which decreases the gap between logic state ON and OFF of an LED, and increases the bit error probability while decoding data from an image. Fortunately, recent developments in deep learning technologies provide us with a new way to approach and deal with this issue. Instead of deriving a complex mathematical algorithm from a pre-defined system model, deep learning, or AI technologies allow the system to learn and approximate an optimizing model directly from training data.