计算机科学
人工智能
感知器
保险丝(电气)
模式识别(心理学)
特征(语言学)
像素
卷积(计算机科学)
波形
块(置换群论)
图像(数学)
特征提取
计算机视觉
人工神经网络
数学
电信
语言学
哲学
雷达
几何学
电气工程
工程类
作者
Changteng Shi,Mengjun Li,Zhiyong An
出处
期刊:PeerJ
[PeerJ]
日期:2024-07-19
卷期号:10: e2208-e2208
标识
DOI:10.7717/peerj-cs.2208
摘要
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.
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