Medical imaging is an essential medical diagnosis system subsequently integrated with artificial intelligence for assistance in clinical diagnosis. The actual medical images acquired during the image capturing procedures generate poor quality images as a result of numerous physical restrictions of the imaging equipment and time constraints. Recently, medical image super-resolution (SR) has emerged as an indispensable research subject in the community of image processing to address such limitations. SR is a classical computer vision operation that attempts to restore a visually sharp high-resolution images from the degraded low-resolution images. In this study, an effective medical super-resolution approach based on weighted least squares optimisation via multiscale convolutional neural networks (CNNs) has been proposed for lesion localisation. The weighted least squares optimisation strategy that particularly is well-suited for progressively coarsening the original images and simultaneously extract multiscale information has been executed. Subsequently, a SR model by training CNNs based on wavelet analysis has been designed by carrying out wavelet decomposition of optimized images for multiscale representations. Then multiple CNNs have been trained separately to approximate the wavelet multiscale representations. The trained multiple convolutional neural networks characterize medical images in many directions and multiscale frequency bands, and thus facilitate image restoration subject to increased number of variations depicted in different dimensions and orientations. Finally, the trained CNNs regress wavelet multiscale representations from a LR medical images, followed by wavelet synthesis that forms a reconstructed HR medical image. The experimental performance indicates that the proposed model SR restoration approach achieve superior SR efficiency over existing comparative methods

医学影像是必不可少的医学诊断系统，随后与人工智能集成以辅助临床诊断。由于成像设备的众多物理限制和时间限制，在图像捕获过程中获取的实际医学图像生成质量较差的图像。最近，医学图像超分辨率 (SR) 已成为图像处理社区中不可或缺的研究课题，以解决此类限制。SR 是一种经典的计算机视觉操作，它试图从退化的低分辨率图像中恢复视觉上清晰的高分辨率图像。在这项研究中，提出了一种基于加权最小二乘优化的有效医学超分辨率方法，通过多尺度卷积神经网络 (CNN) 进行病变定位。加权最小二乘优化策略特别适用于逐步粗化原始图像并同时提取多尺度信息。随后，通过对多尺度表示的优化图像进行小波分解，设计了基于小波分析训练 CNN 的 SR 模型。然后分别训练多个 CNN 以近似小波多尺度表示。经过训练的多卷积神经网络在多个方向和多尺度频带上表征医学图像，从而有助于在不同维度和方向描述的变化数量增加的情况下进行图像恢复。最后，经过训练的 CNN 从 LR 医学图像中回归小波多尺度表示，然后是小波合成，形成重建的 HR 医学图像。实验性能表明，所提出的模型 SR 恢复方法比现有的比较方法实现了更高的 SR 效率