Abstract
Recent studies have shown that effectively combining rich representations of convolution neural network can significantly boost the performance of single image super resolution. Although dense skip connections can aggressively reduce depth and parameter count by feature reuse, it is a memory-intensive fusion operation. In this paper, we proposed a tree-structured deep aggregation block that spans the spectrum of layers to achieve more accuracy with less parameters and memory in super-resolution. Most of methods fuse the all features of blocks by a simple one-step aggregation. But it don’t robust enough for train data with discrepancy. So we propose a recursive aggregation structure to get rich semantic information and perform better on propagation features and gradient. We performed our method on three benchmark datasets and get a comparable result in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural SIMilarity) compared with state-of-the-art methods.
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Chen, X., Lu, Y., Wang, X., Li, W., Wang, Z. (2019). Accurate Single Image Super-Resolution Using Deep Aggregation Network. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_17
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