Single-image super-resolution reconstruction based on phase-aware visual multi-layer perceptron (MLP)

Abstract

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.

Keywords

• Super-resolution reconstruction
• MLP
• Deep learning