Abstract：
Recently, multi-resolution pyramid-based techniques have emerged as the prevailing research approach for image super-resolution. However, these methods typically rely on a single mode of information transmission between levels. In our approach, a wavelet pyramid recursive neural network (WPRNN) based on wavelet energy entropy (WEE) constraint is proposed. This network transmits previous-level wavelet coefficients and additional shallow coefficient features to capture local details. Besides, the parameter of low- and high-frequency wavelet coefficients within each pyramid level and across pyramid levels is shared. A multi-resolution wavelet pyramid fusion (WPF) module is devised to facilitate information transfer across network pyramid levels. Additionally, a wavelet energy entropy loss is proposed to constrain the reconstruction of wavelet coefficients from the perspective of signal energy distribution. Finally, our method achieves the competitive reconstruction performance with the minimal parameters through an extensive series of experiments conducted on publicly available datasets, which demonstrates its practical utility.
摘要：
最近,基于金字塔的多分辨率技术已经成为图像超分辨率的主要研究方法。然而,这些方法通常依赖于级别之间的信息传输的单一模式。在我们的方法中，提出了一种基于小波能量熵(WEE)约束的小波金字塔递归神经网络(WPRNN)。该网络传输先前级别的小波系数和附加的浅系数特征以捕获局部细节。此外,每个金字塔级别和跨金字塔级别的低频和高频小波系数的参数是共享的。设计了多分辨率小波金字塔融合（WPF）模块，以促进跨网络金字塔级别的信息传递。此外,从信号能量分布的角度出发，提出了一种小波能量熵损失来约束小波系数的重构。最后,我们的方法通过在公开可用的数据集上进行的一系列广泛的实验，以最小的参数实现了具有竞争力的重建性能，这证明了它的实际效用。
