基于生成对抗网络的弹载图像盲去模糊算法

The low-speed roll missile seeker has a serious blurring feature in image collection due to its motion characteristics such as dithering and rotating,which directly affects the accuracy of subsequent image algorithms for target recognition,thus affecting the guidance accuracy. To solve the above problems, a blind deblurring algorithm based on a generative adversarial network is proposed. The motion blurring simulation system is used to simulate the motion blurring such as jitter and rotation of missile-borne image,and a fuzzy dataset of missile-borne image is made. The convolution neural network is used as the basic architecture of generator and discriminator,and several loss functions are designed to optimize the network together to reduce the noise and keep the image smooth during image restoration. The de-blurring of missile-borne image is achieved, and a more stable and clear image sequence is obtained. The experimental results show that the proposed algorithm performs better in peak signal-to-noise ratio and structural similarity than other algorithms and achieves state-of-the-art performance,and accords with the subjective perception of human vision. It has practical application value.