Abstract: Image technology is widely used in security, traffic, monitoring, and other social activities. However, these images carrying detailed information will have feature distortion due to various external physical factors in social ingestion, transmission, and storage, resulting in poor image quality and clarity. Resolution determines the definition of an image. Super-resolution reconstruction is the process of transforming a low-resolution picture into a high-resolution image. To enhance the image clarity, this experiment introduces the advantages and disadvantages of the Super Resolution Convolutional Network (SRCNN) and Fast Super Resolution Convolutional Neural Network (FSRCNN) model and then constructs an image super resolution method based on DSRCNN. The algorithm consists of two sub-network blocks, an enhancement block and a purification block. The model first uses two Convolutional Neural Networks (CNN) to obtain complementary low-frequency information that improves the model's learning ability; next, it employs an enhancement block to fuse the image features of two paths via residual operation and sub-pixel convolution to prevent the loss of low-resolution image information; finally, it employs a feature purification block to refine high-frequency information that more accurately represents the predicted high-quality image. It is found that the PSNR and SSIM of the DSRCNN model can reach 33.43dB and 0.9157dB, respectively.

Wenbao Lan and Chang Che. “Research on Image Sharpness Enhancement Technology based on Depth Learning”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.2 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140283

@article{Lan2023,
title = {Research on Image Sharpness Enhancement Technology based on Depth Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140283},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140283},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Wenbao Lan and Chang Che}
}