الفهرس | Only 14 pages are availabe for public view |
Abstract This thesis is concerned with video quality enhancement using dehazing techniques. Dehazing is the process of removing haze from hazy images and enhancing the image contrast. Hazing is defined as a degradation effect in images. It is produced due to light scattering from tiny particles in the atmosphere combined with the sunlight around the scene to be imaged. Hazing resembles a white mask overall image. Most automatic systems, which depend on the description of the input images, suffer from the worst results due to degraded images. To enhance video quality, we use three types of dehazing algorithms in this thesis such as an optimized dehazing algorithm, a recursive deep residual learning (DRL) network, and a dual transmission map-dehazing algorithm. Our proposed algorithm depends on pre-processing of frames before dehazing process to remove noise and reduce dynamic range because all frames contain some noise, and limited dynamic range due to sensor measurement error that can be amplified in the haze removal process if ignored. We use different types of enhancement techniques to remove noise and reduce dynamic range before the dehazing process such as median, homomorphic enhancement, and Frost filter. The video consists of several frames (images). We work on two types of video sequences: a Near Infrared (NIR) and a visible sequence. Results for visible video are more obvious than NIR video because hazy visible frames have a large amount of hazing compared to hazy NIR images. The effect of attenuation parameter of atmosphere α and the effect of attenuation weight ω without and with enhancement visible and NIR; video frames are investigated in our proposed with an optimized dehazing algorithm. We modify the deep residual learning algorithm to generate a new one to be suitable for both NIR and visible frames. The number of iterations in the DRL network is increased from three to nine iterations to study the effect of increasing the number of iterations on the output-dehazed frames. We stopped at nine iterations because elapsed time increases with increasing the iteration number. The effect of a regularization parameter β on visible and NIR dehazed frames without and with homomorphic enhancement is investigated in our proposed dual transmission map-dehazing algorithm. The Peak Signal-to-Noise Ratio (PSNR) and correlation after the dehazing process (between dehazed and input hazy frames) and the spectral entropy of dehazed frames are used as metrics methods for our proposed algorithm. Comparison between the proposed algorithm and different dehazing algorithms on real-world images is achieved. The simulation results prove that enhancement for average results of five frames for visible (riverside.avi), video is 14.74% by dual transmission dehazing technique followed by optimized dehazing by 9.65%. Finally, DRL Network by 9%. For NIR video, the best enhancement by optimized dehazing with 22.28%, after that dual transmission dehazing algorithm by 21.57%. Finally, DRL network by 4.6%. |