# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 5 Conclusions  
In this paper, we fully explore the potential of the gradient prior for SR task. Specifically, to restore sharp high-frequency details, we introduce an additional gradient branch in the classical SR network to provide the beneficial structural features for each upsampling stage of the SR process. Meanwhile, we propose a compact Gradient-Guided (GG) loss to strengthen the constraints on the spatial structure of the reconstructed images, so as to guide the model to restore the appropriate gradient information at the suitable position and to avoid blindly restoring high-frequency details. Additionally, we propose a novel Contextual Residual Fusion Structure (CRFS), which is capable of fully fusing complementary contextual residual features to produce more advanced features; further, to promote the feature representation ability of CRFS as much as possible, we propose an efficient Large-Receptive-field-based Spatial Attention Module (LRSAM) to highlight the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**FLOPs**} & \multirow{2}{*}{**Scale**} & **Set5** & **Set14** & **BSD100** & **Urban100** & **Manga109** \\  & & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline DRN [40] & 5.4M & 171G & & 26.17/0.7704 & 24.43/0.6394 & 24.26/0.5916 & 22.22/0.6315 & 23.70/0.7782 \\ RCAN [29] & 15.74M & 264G & & 26.22/0.7713 & 24.51/0.6415 & 24.28/0.5933 & 22.34/0.6402 & 23.87/0.7806 \\ HAN [12] & 16.22M & 271G & & 26.20/0.7612 & 24.43/0.6346 & 24.28/0.5888 & 22.33/0.6286 & 23.71/0.7629 \\ PA-EDSR [14] & 45.53M & 1271G & 8 & 26.26/0.7727 & 24.51/0.6401 & 24.29/0.5935 & 22.57/0.6460 & 23.85/0.7780 \\ NLSN [11] & 46.52M & 738G & & 25.91/0.7544 & 24.04/0.6245 & 24.10/0.5840 & 21.80/0.6062 & 23.00/0.7465 \\ GPSR & 14.42M & 382G & & 26.32/0.7788 & 24.60/0.6464 & 24.36/0.5974 & 22.85/0.6607 & 24.25/0.7950 \\ GPSR+ & 14.42M & - & 26.42/0.7809 & 24.72/0.6491 & 24.42/0.5987 & 23.05/0.6656 & 24.50/0.7993 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative metrics obtained using various models for \(\times\)8 SR task with BD degradation model. The optimal and suboptimal metrics are highlighted in red and blue. critical residual features; finally, we incorporate LRSAM into CRFS and further propose two Residual Spatial Attention Adaptive Aggregation Modules (RS3 AMs) used for image feature extraction and gradient feature extraction, respectively. Extensive experiments indicate that the superiority of our Gradient-Prior-based Super-Resolution network (GPSR) for images over state-of-the-art methods for SR tasks with large scaling factors (\(\times 4\) and \(\times 8\)). However, there are two limitations to our method, as follows: (1) Although our GPSR achieves better results with fewer parameters and FLOPs than most existing SR methods, its computational efficiency still needs to be further improved to satisfy the requirements of real applications, such as the deployment of the technique in embedded devices. (2) Like most existing SR methods, the proposed GPSR only considers known degradation models (e.g., BI and BD degradation kernels), and it is still struggling with SR tasks with unknown degradation kernels. To address these two limitations, we will focus on compressing the model to improve computational efficiency while retaining SR performance in our future work. Moreover, we will explore the introduction of the adversarial generative network into the SR task to estimate the unknown degradation kernels, which facilitates the real-world image SR. X.Z. : Conceptualization, Methodology, and Writing. D.H.: Supervision, Review, and Editing. X.L. : Software and Visualization. D.C.: Original draft preparation. D.Z. : Editing and Review. All authors have read and agreed to the published version of the manuscript.