Improved immune algorithm for image restoration
Improved immune algorithm for image restoration
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发表时间：
2009
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通讯作者：
Wu Le-nan
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作者：
Wu Le-nan
为了提高图像超分辨率复原的性能，提出了一种包含广义高斯白分布噪声和各向异性正则化的新模型。为了获得新模型的最优解，提出了免疫算法，并从三个方面对其进行了改进：引入记忆单元组，使算法在两个独立的组上并行执行；提出了一种输出疫苗和接种的自适应方法；分析和实验结果表明，基于该模型的恢复算法对不同类型的噪声和噪声的变化都具有较强的鲁棒性，恢复图像的信噪比(ISNR)比传统模型提高了1.5dB.同时，改进的免疫算法能够快速收敛，迭代步数是遗传算法总步数的8%，免疫算法总步数的40%.该模型和改进免疫算法组成的系统在超分辨率恢复方面是可靠的.
In order to improve the performance of super-resolution restoration for an image,a new model involving general white Gaussian distributed noise and anisotropy regularization is presented.To acquire the optimal solution of the new model,immune algorithm is proposed and improved in three aspects: a memory unit group is introduced to make the algorithm perform on two independent groups parallelly; an adaptive method of exporting vaccines and inoculation is presented;and a chaos operator is implanted to the algorithm for anti-freezing.Analysis and experimental results demonstrate that the restoration based on this proposed model is robust both to different types of noises and to variances of noises.Moreover,the Improved Ratio of Signal to Noise(ISNR) of the restored image using this proposed model is 1.5 dB higher than that using traditional model.Meanwhile,improved immune algorithm can converge fastly,the steps of iteration are 8% that of the total steps for GA,and 40% that of the total steps for immune algorithm.The system consisting of the proposed novel model and improved immune algorithm is reliable on the super-resolution restoration.