[論文レビュー] Wide Activation for Efficient and Accurate Image Super-Resolution

Jiahui Yu, Yuchen Fan+5 more|arXiv (Cornell University)|Aug 27, 2018

Advanced Image Processing Techniques参考文献 8被引用数 324

ひとこと要約

論文は、SR残差ブロック（WDSR-AおよびWDSR-B）においてReLUの前に特徴チャネルを広げることで、同じパラメータ/計算予算で精度が向上することを示しており、線形低ランク畳み込みによりさらに広い活性化が可能になり、重み正規化がバッチ正規化や正規化なしの設定を上回っている。

ABSTRACT

In this report we demonstrate that with same parameters and computational budgets, models with wider features before ReLU activation have significantly better performance for single image super-resolution (SISR). The resulted SR residual network has a slim identity mapping pathway with wider (\(2 imes\) to \(4 imes\)) channels before activation in each residual block. To further widen activation (\(6 imes\) to \(9 imes\)) without computational overhead, we introduce linear low-rank convolution into SR networks and achieve even better accuracy-efficiency tradeoffs. In addition, compared with batch normalization or no normalization, we find training with weight normalization leads to better accuracy for deep super-resolution networks. Our proposed SR network extit{WDSR} achieves better results on large-scale DIV2K image super-resolution benchmark in terms of PSNR with same or lower computational complexity. Based on WDSR, our method also won 1st places in NTIRE 2018 Challenge on Single Image Super-Resolution in all three realistic tracks. Experiments and ablation studies support the importance of wide activation for image super-resolution. Code is released at: https://github.com/JiahuiYu/wdsr_ntire2018

研究の動機と目的

• Reproduce that wider activation before ReLU improves SR performance without extra parameters or FLOPs.
• Propose WDSR-A (wide activation) and WDSR-B (wide activation with linear low-rank convolutions) for better accuracy-efficiency.
• Show that weight normalization provides faster convergence and better accuracy than batch normalization or no normalization.
• Evaluate on DIV2K and achieve strong results and NTIRE 2018 wins across tracks.

提案手法

• Introduce wider activation by expanding features before ReLU within SR residual blocks.
• Maintain the identity mapping pathway width while increasing the pre-activation width to create WDSR-A.
• Develop WDSR-B with linear low-rank convolutions to widen activation further without extra cost.
• Compare with EDSR under identical parameter/compute budgets to isolate the effect of width.
• Advocate weight normalization over batch normalization for training deep SR networks and demonstrate faster convergence.

実験結果

リサーチクエスチョン

• RQ1 Does widening features before ReLU in SR residual blocks improve accuracy under fixed parameter and compute budgets?
• RQ2 Can efficiency be maintained or improved when widening activation using linear low-rank convolutions?
• RQ3 Is weight normalization more suitable than batch normalization for training deep SR networks?
• RQ4 What gains do WDSR-A and WDSR-B achieve on standard SR benchmarks (e.g., DIV2K) compared to baselines like EDSR?

主な発見

• WDSR-A (2x–4x wider activation) improves DIV2K val PSNR over EDSR at the same budget.
• WDSR-B (6x–9x wider activation) uses linear low-rank convolutions to widen activation with no extra parameters or FLOPs and yields further gains.
• On DIV2K, WDSR variants achieve higher PSNR than the corresponding EDSR baselines at equivalent parameter counts (example results show 33.210 vs. 33.434 PSNR for EDSR and WDSR-B in the 1-residual-block setting).
• Weight normalization accelerates convergence and improves accuracy, while batch normalization leads to instability during testing in SR tasks.