Adaptive Residual Networks for High-Quality Image Restoration

Area of Science:

• Computer Vision
• Deep Learning
• Image Processing

Background:

• Convolutional neural networks (CNNs) are successful in image restoration but often lack depth for optimal performance.
• Existing deep networks with shortcuts may neglect input location and scaling, limiting their application scope.
• Current methods are typically designed for single image restoration tasks.

Purpose of the Study:

• To propose a novel adaptive residual network (ARN) for high-quality image restoration.
• To develop a versatile deep learning model capable of addressing multiple image restoration challenges.
• To improve upon the performance limitations of existing deep neural network architectures in image restoration.

Main Methods:

• Developed a deep residual network (ARN) incorporating convolutional layers, parametric rectified linear unit (PReLU) layers, and adaptive shortcuts.
• Implemented adaptive scaling parameters within the shortcuts, trained dynamically for different applications.
• Evaluated the ARN model on Gaussian image denoising, single image super-resolution, and JPEG image deblocking.

Main Results:

• The proposed ARN model demonstrated superior performance across multiple image restoration tasks.
• Achieved significant improvements in peak signal-to-noise ratio (PSNR) and structure similarity index (SSIM) metrics.
• Outperformed state-of-the-art restoration methods, showing an average gain of 0.2-0.3 dB.

Conclusions:

• The novel adaptive residual network (ARN) offers a versatile and high-performance solution for diverse image restoration problems.
• Adaptive scaling in network shortcuts is crucial for enhancing performance and applicability.
• ARN represents a significant advancement in deep learning for image restoration.