Physics

Pattern recognition (psychology)

Noise reduction

Image resolution

Feature (linguistics)

Operating system

Image (mathematics)

Software deployment

Computer engineering

Philosophy

Shuffling

Artificial intelligence

Exploit

Programming language

Scale (ratio)

Computer science

Linguistics

Feature extraction

Quantum mechanics

Computer vision

Computer security

Artificial neural network

During the past years, tremendous advances in image restoration tasks have been achieved using highly complex neural networks. Despite their good restoration performance, the heavy computational burden hinders the deployment of these networks on constrained devices, \eg smart phones and consumer electronic products. To tackle this problem, we propose a self-guided network (SGN), which adopts a top-down self-guidance architecture to better exploit image multi-scale information. SGN directly generates multi-resolution inputs with the shuffling operation. Large-scale contextual information extracted at low resolution is gradually propagated into the higher resolution sub-networks to guide the feature extraction processes at these scales. Such a self-guidance strategy enables SGN to efficiently incorporate multi-scale information and extract good local features to recover noisy images. We validate the effectiveness of SGN through extensive experiments. The experimental results demonstrate that SGN greatly improves the memory and runtime efficiency over state-of-the-art efficient methods, without trading off PSNR accuracy.

Self-Guided Network for Fast Image Denoising