Adaptive Densely Connected Super-Resolution Reconstruction
This work addresses image quality enhancement for applications like medical imaging or surveillance, but it appears incremental as it builds on existing dense connection techniques.
The paper tackles single image super-resolution by proposing an adaptive densely connected algorithm (ADCSR) with BODY and SKIP parts, achieving superior performance verified by PSNR, SSIM, and visual comparisons to state-of-the-art methods.
For a better performance in single image super-resolution(SISR), we present an image super-resolution algorithm based on adaptive dense connection (ADCSR). The algorithm is divided into two parts: BODY and SKIP. BODY improves the utilization of convolution features through adaptive dense connections. Also, we develop an adaptive sub-pixel reconstruction layer (AFSL) to reconstruct the features of the BODY output. We pre-trained SKIP to make BODY focus on high-frequency feature learning. The comparison of PSNR, SSIM, and visual effects verify the superiority of our method to the state-of-the-art algorithms.