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Single image super-resolution (SISR) aims to reconstruct a high-resolution image from a degraded low-resolution image. In recent years, the super-resolution methods based on convolutional neural network (CNN) have achieved promising performance on SISR task, indicating that CNN is a viable approach to image super-resolution reconstruction. The one limitation of the current SISR methods is that many methods use the pixel-wise loss. It is well known that the pixel-wise loss cannot well recover high-frequency details even if the high peak signal-to-noise ratio (PSNR) can be obtained. Some other methods purely focus on restoring more details, which resulted in poor PSNR score and high-frequency noise. In this paper, we proposed a multi-component loss function based on pixel-wise loss, perceptual loss and adversarial loss for a multi-scale feature mapping generator network for SISR image reconstruction model. We evaluated our method on commonly used benchmarks and compared it with other SISR methods. The results showed that our method could achieve the better balance between the high-frequency detail and stable spatial structure generation.