Adaptive Contrast Based Real-Time Image and Video Dehazing

Dehazing algorithms have been developed in re-sponse to the need for effectively and instantaneously removing atmospheric turbidities such as mist, haze, and fog from media. The removal of haze from an image or video enables the extraction of additional details from the scene. This paper presents the development of a real-time, memory-optimized dehazing system that utilizes digital image processing techniques and NVIDIA's CUDA architecture for efficient parallel computing. The methodology incorporates a quad-tree search algorithm for efficient atmospheric light estimation, which significantly enhances dehazing accuracy. Advanced contrast enhancement techniques are employed for transmission estimation to ensure clarity and visibility in dehazed images, addressing challenges such as non-uniform illumination and varying haze densities. For image restoration, the system dynamically clips values to improve brightness and minimize information loss. The transmission map's artifacts are then smoothed using a Gaussian filter, resulting in more reliable dehazing. Extensive testing on a variety of publicly accessible datasets demonstrated that the proposed model achieved comparable accuracy to numerous existing techniques, while also restoring high-quality dehazed images and videos. The system achieves a running time of 35ms per image and up to 10ms per frame for video sequences. Performance was objectively assessed using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), yielding a PSNR of 28.75 and an SSIM of 0.80.