CAJ | 학술논문

针对模糊图像高维空间几何信息（high-dimensional space geometrical informatics，HDSGI）复原方法不能自动调节参数的问题，提出一种结合混沌粒子群优化（chaotic particle swarm optimization，CPSO）算法进行模糊图像自适应复原的新方法。HDSGI图像复原算法可以获得清晰的复原图像，但是需要人工调节表征分布曲线的参数，参数选择不合适时复原图像中会出现噪声。将能同时度量图像模糊程度和噪声水平的无参考型图像质量评价指标作为CPSO算法的适应度函数，达到自适应地选择最佳分布曲线的目的，从而可以获得清晰复原图像。复原后的图像的主观视觉评价和定量评价指标均证明了方法的实用性和有效性。
침대모호도상고유공간궤하신식（high-dimensional space geometrical informatics，HDSGI）복원방법불능자동조절삼수적문제，제출일충결합혼돈입자군우화（chaotic particle swarm optimization，CPSO）산법진행모호도상자괄응복원적신방법。HDSGI도상복원산법가이획득청석적복원도상，단시수요인공조절표정분포곡선적삼수，삼수선택불합괄시복원도상중회출현조성。장능동시도량도상모호정도화조성수평적무삼고형도상질량평개지표작위CPSO산법적괄응도함수，체도자괄응지선택최가분포곡선적목적，종이가이획득청석복원도상。복원후적도상적주관시각평개화정량평개지표균증명료방법적실용성화유효성。
For the problem that the high-dimensional space geometrical informatics (HDSGI)blurred image restoration method fails to adjust the parameters automatically,a new blurred image restoration method which combines the HDSGI theory with the chaotic particle swarm optimization (CPSO)algorithm is proposed.Based on the HDSGI theory,the clear restored image can be obtained,while the parameters of the distribution curve in the above method need to be regulated manually and the restored image may result in noise with inappropriate parameters.In this paper,a no-reference quality assessment method,which can measure both noise levels and blurred degrees of images,is adopted as the fitness function of the CPSO algorithm to find the best distribution curve automatically,thus the best restored image is obtained.The subjective vision assessment and the objective quantitative assessment of images demonstrate that the proposed method is practical and effective.