In the field of deep space science detection and high resolution earth observation, a relatively high motion velocity is often generated between the optical camera and the imaging target. Images obtained during the exposure time can produce image motion blur, which becomes one of the main obstacles to acquire high resolution image near the target. As an extended task of the third phase of China’s lunar exploration program, flight imaging of the planned sampling area of Chang’e-5 was carried out. A dual resolution camera with a wide field of view (FOV) camera and a narrow FOV camera was used for imaging mission. High flying speed causes the generation of large motion blurred images captured by the narrow FOV camera and the motion blur can be up to around 30 pixels. To deal with this problem, we analyzed the image features of the blurred images captured by the narrow FOV camera, and proposed a corresponding method that can estimate image motion value from the blurred lunar image based on small craters detection scheme and then adopted the regularization method to restore the image. The algorithm is applied in the batch processing of the real blurred lunar images and has achieved a significant restored effect.
In this paper, we present a method for single image blind deconvolution. Many common forms of blind deconvolution methods need to previously generate a salient image, while the paper presents a novel L0 sparse expression to directly solve the ill-positioned problem. It has no need to filter the blurred image as a restoration step and can use the gradient information as a fidelity term during optimization. The key to blind deconvolution problem is to estimate an accurate kernel. First, based on L2 sparse expression using gradient operator as a prior, the kernel can be estimated roughly and efficiently in the frequency domain. We adopt the multi-scale scheme which can estimate blur kernel from coarser level to finer level. After the estimation of this level’s kernel, L0 sparse representation is employed as the fidelity term during restoration. After derivation, L0 norm can be approximately converted to a sum term and L1 norm term which can be addressed by the Split-Bregman method. By using the estimated blur kernel and the TV deconvolution model, the final restoration image is obtained. Experimental results show that the proposed method is fast and can accurately reconstruct the kernel, especially when the blur is motion blur, defocus blur or the superposition of the two. The restored image is of higher quality than that of some of the art algorithms.
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