Y channel (in YCbCr color space), as it is a common practice in the SR literature [@RCAN; @RealSR; @wang2019deep; @anwar2019deep]. The results in Table \[table:realSR\] show that the bicubic interpolation provides the least accurate results, thereby indicating its low suitability for dealing with real images. Moreover, the same table shows that the recent method LP-KPN [@RealSR] provides marginal improvement of only $\sim0.04$ dB over the previous best method RCAN [@RCAN]. In contrast, our method significantly advances state-of-the-art and consistently yields better image quality scores than other approaches for all three scaling factors. Particularly, compared to LP-KPN [@RealSR], our method provides performance gains of $0.45$ dB, $0.74$ dB, and $0.22$ dB for scaling factors $\times2$, $\times3$ and $\times4$, respectively. The trend is similar for the SSIM metric as well.

Visual comparisons in Fig. \[fig:sr example\] show that our MIRNet recovers content structures effectively. In contrast, VDSR [@VDSR], SRResNet [@SRResNet] and RCAN [@RCAN] reproduce results with noticeable artifacts. Furthermore, LP-KPN [@RealSR] is not able to preserve structures (see near the right edge of the crop). Several more examples are provided in Fig. \[fig:sr crop examples\] to further compare the image reproduction quality of our method against the previous best method [@RealSR]. It 