EPSR: Edge Profile Super resolution
This work addresses the challenge of texture loss and structure preservation in image super-resolution, which is important for applications like medical imaging or video enhancement, but it appears incremental as it builds on existing network architectures.
The paper tackles the problem of preserving structure and restoring texture in super-resolution by proposing the Edge Profile Super Resolution (EPSR) method, which achieves competitive performance in PSNR and SSIM metrics compared to state-of-the-art methods.
In this paper, we propose Edge Profile Super Resolution(EPSR) method to preserve structure information and to restore texture. We make EPSR by stacking modified Fractal Residual Network(mFRN) structures hierarchically and repeatedly. mFRN is made up of lots of Residual Edge Profile Blocks(REPBs) consisting of three different modules such as Residual Efficient Channel Attention Block(RECAB) module, Edge Profile(EP) module, and Context Network(CN) module. RECAB produces more informative features with high frequency components. From the feature, EP module produce structure informed features by generating edge profile itself. Finally, CN module captures details by exploiting high frequency information such as texture and structure with proper sharpness. As repeating the procedure in mFRN structure, our EPSR could extract high-fidelity features and thus it prevents texture loss and preserves structure with appropriate sharpness. Experimental results present that our EPSR achieves competitive performance against state-of-the-art methods in PSNR and SSIM evaluation metrics as well as visual results.