The research paper focuses on a challenging task faced in blind image deblurring (BID). It relates to the estimation of arbitrarily shaped (nonparametric or complex shaped) point spread functions (PSFs) of motion blur caused by camera handshake. These PSFs exhibit much more complex shapes than their parametric counterparts and deblurring, in this case, requires intricate ways to estimate the blur and effectively remove it. This research work introduces a novel blind deblurring scheme visualized for deblurring images corrupted by arbitrarily shaped PSFs. It is based on genetic algorithm and utilizes the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) measure as the fitness function for arbitrarily shaped PSF estimation. The proposed BID scheme has been compared with other state-of-the-art single image motion deblurring schemes as benchmarks. Validation has been carried out on the standard real-life blurred images. Results of both benchmark and real images are presented. For real-life blurred images, the proposed BID scheme using BRISQUE converges in close vicinity of the original blurring functions. However, the benchmark schemes fail to effectively restore the real blurred images. The proposed scheme surpasses on average of seven percent higher image quality as compared to the benchmark schemes.

中文翻译：

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用于单幅图像盲去模糊的任意形状的点扩展函数 (PSF) 估计

该研究论文侧重于盲图像去模糊 (BID) 中面临的一项具有挑战性的任务。它涉及对由相机握手引起的运动模糊的任意形状（非参数或复杂形状）点扩散函数 (PSF) 的估计。这些 PSF 表现出比参数化对应物复杂得多的形状，在这种情况下，去模糊需要复杂的方法来估计模糊并有效地去除它。这项研究工作介绍了一种新颖的盲去模糊方案，该方案可视化用于对由任意形状的 PSF 损坏的图像进行去模糊。它基于遗传算法，并利用盲/无参考图像空间质量评估器 (BRISQUE) 度量作为任意形状 PSF 估计的适应度函数。所提出的 BID 方案已与其他最先进的单图像运动去模糊方案作为基准进行了比较。已经对标准的现实生活中的模糊图像进行了验证。显示了基准图像和真实图像的结果。对于现实生活中的模糊图像，所提出的使用 BRISQUE 的 BID 方案收敛于原始模糊函数的附近。然而，基准方案未能有效地恢复真实的模糊图像。与基准方案相比，所提出的方案的图像质量平均高出 7%。然而，基准方案未能有效地恢复真实的模糊图像。与基准方案相比，所提出的方案的图像质量平均高出 7%。然而，基准方案未能有效地恢复真实的模糊图像。与基准方案相比，所提出的方案的图像质量平均高出 7%。