Rearch Of Digital Image Dehazing And Super-resolution Reconstruction

In most applications, people need orexpect higher quality images. But now most of vision system, especially for the video monitering system, due to the imaging system, the external environment, imaging technologies, storage technologies, network speed, and many other factors, result in image degradation or forced, is not rich in detail and suffer from contrast reduction, so that image visual effect obtained is not ideal, of subjective observations and objective evaluation. However, in practice, the quality of image we obtained are increasing, especially for video images used for later evidence, which requires the ability to toaccurately identify images of human faces or license plates and other interesting targets. So, how to effectively restore the original image or a recovery that we expect the quality of the image, improving image clarity, the video surveillance system has become a very important and difficult problem.For now, the video monitering system for clarity of image processing techniques include: image defog and super-resolution reconstruction. Image defoge restore image image contrast and true colors, ideal weather conditions to reproduces clear images taken. Super-resolution technique is the actual imaging system to compensate for the limitations of hardware through the signal processing method, the degradation of low-resolution image fusion for the better quality high-resolution images, and remove the fuzzy low-resolution images and noise. In this paper, regard to the two key technologies of image processing field based on many research results of experts and scholars at home and abroad. Major work are completed as follows:1) In regard to the defog, Images captured in fog suffer from poor constrst and visibility. It is important to remove weather effects from the degraded image in order to make vision systems more reliable and more robust. A inproved method is proposed to overcome the weakness of original algorithm. By including tolerance mechanism and adaptive efforts to fog, inproved algorithm can effectively deal with the sky, white object, and so bright area that do not meet the assumptions of dark channel, to overcome color distortion which is arise by using the original algorithm dealing with these areas. Experimental results show that such changes is feasible, to eliminate color distortion of the image, visibility can be significantly enhanced.2) In regard to the Super-resolution image reconstruction, produces a high-resolution image from a set of low-resolution images. POCS algorithm is a widely used algorithm based set theory. In this paper, a novel spatio-temporal adaptive super-resolution reconstruction algorithm of video based on POCS frame is proposed to overcome the weakness of conventional POCS algorithms. The spatio-temporal adaptive mechanism, which is induced to POCS super-resolution reconstruction frame, can prevent reconstructed image from the influence of inaccurate motion information to some extent and avoid the effect of noise amplification, which exist in using conventional POCS algorithms to reconstruct video sequences with dramatic target motion. Experimental results show that the proposed algorithm can effectively alleviate amplification noise and is better than the traditional method both in vision effect and signal to noise ration.In this paper, we studied two key techniques, enhance the performance of the algorithm to do a useful experiment to try and demonstrate, these attempts to improve the monitering system enhance the system reliability and robustness of great significance and problems related to the field.