Intelligent Real-Time Low-Light Vision System: Retinex-Based Enhancement Using Raspberry Pi 4

Low-light image enhancement is an essential step for sustaining computer vision, which can be employed in various fields like surveillance, robotics, and IoT-based applications. Poor illumination drastically lowers the visual perception and decision-making capabilities of these systems. This work is about a smart real-time low-light vision system, which uses a Retinex-based enhancement framework to carry out the function in Raspberry Pi 4. Raspberry Pi 4 was chosen for the work because of its quad-core processor, enough RAM, low power consumption, small size, and good compatibility with Python and OpenCV, all of which make real-time embedded vision processing efficient and feasible. The device utilized for image capture is the Raspberry Pi Camera Module Rev 1.3 (5 MP OV5647 sensor), which supports$2592 \times 1944$resolution for images and real-time video capture up to$\text{1 0 8 0 p}$at$\text{3 0 ~ f p s}$. The proposed system utilizes a Retinex-based algorithm that is optimized for embedded deployment to enhance the illumination and also keep the reflectance details unchanged. The results of the experiments show that the enhancement is effective with PSNR$\approx 24.15 ~\text{dB}$,$\text{SSIM}=0.81, \text{CII}=1.40$, and entropy increase of 1.40 bits (7.50 - 6.10), thus enabling real-time processing at 8 frames per second. Such results are indicative of the potential of the system to address low-light vision challenges in real-world scenarios. Plans for future work hardware acceleration, higher frame-rate optimization, outdoor deployment, and deep learning-based enhancement techniques integration for increased robustness.