The field of image deblurring has seen tremendous progress with the rise of deep learning models. These models, albeit efficient, are computationally expensive and energy consuming. Dictionary based learning approaches have shown promising results in image denoising and Single Image Super-Resolution. We propose an extension of the Rapid and Accurate Image Super-Resolution (RAISR) algorithm introduced by Isidoro, Romano and Milanfar for the task of out-of-focus blur removal. We define a sharpness quality measure which aligns well with the perceptual quality of an image. A metric based blending strategy based on asset allocation management is also proposed. Our method demonstrates an average increase of approximately 13% (PSNR) and 10% (SSIM) compared to popular deblurring methods. Furthermore, our blending scheme curtails ringing artefacts post restoration.

翻译：随着深度学习模型的兴起，图像去模糊领域取得了巨大进展。这些模型虽然高效，但计算成本高昂且能耗较大。基于字典的学习方法在图像去噪和单图像超分辨率任务中已展现出良好效果。本文针对去焦模糊去除任务，对Isidoro、Romano和Milanfar提出的快速准确图像超分辨率（RAISR）算法进行了扩展。我们定义了一种与图像感知质量高度吻合的锐度评价指标，并提出基于资产分配管理的度量融合策略。实验表明，与主流去模糊方法相比，本方法在PSNR指标上平均提升约13%，SSIM指标平均提升约10%。此外，所提出的融合方案有效抑制了复原后产生的振铃伪影。