Aiming to solving the problems of feature redundancy and blurred edge texture in images reconstructed via some of the existing image super-resolution reconstruction algorithms, an image super-resolution reconstruction network with spatial/high-frequency dual-domain feature saliency is proposed. First, the network constructs a feature distillation refinement module to reduce feature redundancy via introduction of blueprint separable convolution, and it then designs parallel dilated convolutions to refine extraction of multiscale contextual features so as to reduce feature loss and compensate for loss of texture in local regions. Second, a spatial dual-domain fusion attention mechanism is designed to enhance the high-frequency feature expression for fully capturing long-range dependency between different locations and channels of the feature map while facilitating reconstruction of edge texture details. The experimental results demonstrate that with its reconstructed image quality, the proposed model outperforms other comparison algorithms both in terms of objective metrics and subjective perception on multiple datasets. At a scaling factor of 2, compared with the VapSR, SMSR, and EDSR, the proposed method enhances the peak signal-to-noise ratio (PSNR) by an average of 0.14 dB, 0.36 dB, and 0.35 dB, respectively.