改进的压缩感知红外图像去噪算法

Improved compressed sensing infrared image denoising algorithm

红外成像系统因其在信号的传输和信号转换环节中会受到外界环境中的各种干扰,使红外成像系统生成的红外图像中产生多种噪声,导致红外图像的信噪比降低。针对红外图像受到多类噪声影响的问题,基于压缩感知理论的迭代加权最小二乘算法设计了新的加权系数,并结合中值滤波算法设计了新的重构算法。首先利用中值滤波对信号进行红外图像的粗去噪,然后通过压缩感知的稀疏变换和观测矩阵进行细去噪,使观测值保留原信号的重要信息,最后通过重构算法得到去噪后的图像。在MATLAB中对算法进行仿真实验,验证算法的有效性。实验表明,该算法得到的图像的视觉效果接近原图像,在实际场景中有较好的去噪性能,峰值信噪比高于原SP算法高3~8 dB,高于迭代加权最小二乘算法5~13 dB。

Because the infrared imaging system will be disturbed by various kinds of external environment in the signal transmission and signal conversion, the infrared imaging system will produce many kinds of noise in the infrared image generated, which will lead to the decrease of the signal-to-noise ratio of the infrared image.In view of the problem that infrared images are affected by multiple types of noise, to make a new weighted coefficient based on the iteratively reweighted least squares algorithm of compressed sensing theory and combined the principle of median filtering to construction a new reconstruction algorithm.Firstly, the median filter is used to do rough denoising of infrared image.Then fine denoising by compressing the sensing sparse transformation and observation matrix to keeping the observations important information about the original signal.Finally, the denoised by reconstruction algorithm.The algorithm is simulated in MATLAB to verify the effectiveness of the algorithm.Experiments show that the visual effect of the image obtained by this algorithm is close to the original image, and it has better denoising performance in the actual scene. The peak signal-to-noise ratio is 3~8 dB higher than the original SP algorithm and 5~13 dB higher than the iterative weighted least square algorithm.