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Edge Profile Super Resolution
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AI
DOI：10.1109/ACCESS.2021.3108998.png)
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摘要
En 中文
The purpose of single-image super resolution (SISR) is to reconstruct an accurate high-resolution image from a degraded low-resolution image. Owing to the lack of information in low-resolution images, SISR is a challenging problem. In particular, it is difficult to represent details, including high-frequency components, such as texture and structural information. We propose the edge profile super-resolution (EPSR) method to preserve structural information and restore texture. EPSR is achieved by stacking modified fractal residual network (mFRN) structures hierarchically and repeatedly. Each mFRN is composed of many residual edge profile blocks (REPBs) that extract features and preserve the edge, structure, and texture information of the image. For implementing REPB, we design three main modules: Residual Efficient Channel Attention Block(RECAB) module, Edge Profile(EP) module, and Context Network(CN) module. By repeating the procedure in the mFRN structure, the EPSR method could be used to extract high-fidelity features, thus preventing texture loss and preserving the structure with appropriate sharpness. Experimental results show that EPSR achieves competitive performance against state-of-the-art methods in terms of the peak signal-to-noise ratio(PSNR) and structural similarity index measure(SSIM) evaluation metrics, as well as visual results.
Keyword:
Image edge detection
Feature extraction
Iron
Image restoration
High frequency
Fractals
Deep learning
Image super-resolution
contextual information
edge
image structure
texture
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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