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压缩感知理论将信号采样和压缩同时进行,且采样频率远低于奈奎斯特频率,为低分辨率采样高分辨率成像提供了可能。为此,提出一种基于CCD图像传感器的压缩成像方法,利用CCD图像传感器模拟像素值串行输出不可重复使用的特点,对图像进行单次测量,构造半循环半随机测量矩阵对CCD图像传感器输出的模拟值进行压缩测量,基于增广拉格朗日法和交替方向法的最小全变分算法(TVAL3)算法解压缩重构图像。该成像方法测量矩阵的稀疏性较强,能较好地恢复原始图像,同时模拟/数字负担及量化编码的复杂度大大降低,成像系统结构简单,实用性强。仿真结果表明,所提成像算法重构的图像主客观质量较好。
Compressive sensing theory takes signal sampling and compression simultaneously, and the sampling frequency is much lower than the Nyquist frequency, which makes it possible for high resolution imaging with low resolution. Therefore, a compression imaging method based on CCD image sensor is proposed. By using CCD image sensor to simulate the non-repeatable use of pixel value serial output, a single measurement of the image is carried out to construct a half-cycle semi-random measurement matrix for CCD image sensor output The compressed images were decompressed by the TVAL3 algorithm based on augmented Lagrangian and Alternating Direction methods. The imaging method has strong sparsity of the measurement matrix and can recover the original image well. Simultaneously, the complexity of analog / digital burden and quantization coding is greatly reduced. The imaging system is simple in structure and practical. The simulation results show that the proposed algorithm has better subjective and objective image quality.