Shi Changteng, Li Mengjun, An Zhiyong
Shandong Technology and Business University, Yantai, China.
PeerJ Comput Sci. 2024 Jul 19;10:e2208. doi: 10.7717/peerj-cs.2208. eCollection 2024.
Many advanced super-resolution reconstruction methods have been proposed recently, but they often require high computational and memory resources, making them incompatible with low-power devices in reality. To address this problem, we propose a simple yet efficient super-resolution reconstruction method using waveform representation and multi-layer perceptron (MLP) for image processing. Firstly, we partition the original image and its down-sampled version into multiple patches and introduce WaveBlock to process these patches. WaveBlock represents patches as waveform functions with amplitude and phase and extracts representative feature representations by dynamically adjusting phase terms between tokens and fixed weights. Next, we fuse the extracted features through a feature fusion block and finally reconstruct the image using sub-pixel convolution. Extensive experimental results demonstrate that SRWave-MLP performs excellently in both quantitative evaluation metrics and visual quality while having significantly fewer parameters than state-of-the-art efficient super-resolution methods.
最近已经提出了许多先进的超分辨率重建方法，但它们通常需要高计算和内存资源，这使得它们在现实中与低功耗设备不兼容。为了解决这个问题，我们提出了一种简单而有效的超分辨率重建方法，该方法使用波形表示和多层感知器（MLP）进行图像处理。首先，我们将原始图像及其下采样版本划分为多个补丁，并引入WaveBlock来处理这些补丁。WaveBlock将补丁表示为具有幅度和相位的波形函数，并通过动态调整令牌之间的相位项和固定权重来提取代表性特征表示。接下来，我们通过特征融合块融合提取的特征，最后使用子像素卷积重建图像。大量实验结果表明，SRWave-MLP在定量评估指标和视觉质量方面都表现出色，同时与最先进的高效超分辨率方法相比，参数显著减少。
