Image deblurring is an essential image preprocessing technique, aiming to recover clear and detailed images form blurry ones. However, existing algorithms often fail to effectively integrate multi-scale feature extraction with frequency enhancement, limiting their ability to reconstruct fine textures. Additionally, non-uniform blur in images also restricts the effectiveness of image restoration. To address these issues, we propose a multi-scale frequency enhancement network (MFENet) for blind image deblurring. To capture the multi-scale spatial and channel information of blurred images, we introduce a multi-scale feature extraction module (MS-FE) based on depthwise separable convolutions, which provides rich target features for deblurring. We propose a frequency enhanced blur perception module (FEBP) that employs wavelet transforms to extract high-frequency details and utilizes multi-strip pooling to perceive non-uniform blur, combining multi-scale information with frequency enhancement to improve the restoration of image texture details. Experimental results on the GoPro and HIDE datasets demonstrate that the proposed method achieves superior deblurring performance in both visual quality and objective evaluation metrics. Furthermore, in downstream object detection tasks, the proposed blind image deblurring algorithm significantly improves detection accuracy, further validating its effectiveness androbustness in the field of image deblurring.

翻译：图像去模糊是一项重要的图像预处理技术，旨在从模糊图像中恢复清晰且细节丰富的图像。然而，现有算法往往未能有效整合多尺度特征提取与频率增强，限制了其重建精细纹理的能力。此外，图像中的非均匀模糊也制约了图像复原的效果。为解决这些问题，我们提出了一种用于盲图像去模糊的多尺度频率增强网络（MFENet）。为捕捉模糊图像的多尺度空间与通道信息，我们引入了基于深度可分离卷积的多尺度特征提取模块（MS-FE），该模块为去模糊任务提供了丰富的目标特征。我们提出了一种频率增强的模糊感知模块（FEBP），该模块利用小波变换提取高频细节，并采用多条形池化感知非均匀模糊，将多尺度信息与频率增强相结合，以改善图像纹理细节的复原。在GoPro和HIDE数据集上的实验结果表明，所提方法在视觉质量和客观评价指标上均取得了优越的去模糊性能。此外，在下游目标检测任务中，所提盲图像去模糊算法显著提升了检测精度，进一步验证了其在图像去模糊领域的有效性和鲁棒性。