Asymmetric CNN for image super-resolution (2103.13634v2)

Abstract: Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. According to nature of different applications, designing appropriate CNN architectures is developed. However, customized architectures gather different features via treating all pixel points as equal to improve the performance of given application, which ignores the effects of local power pixel points and results in low training efficiency. In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. The AB utilizes one-dimensional asymmetric convolutions to intensify the square convolution kernels in horizontal and vertical directions for promoting the influences of local salient features for SISR. The MEB fuses all hierarchical low-frequency features from the AB via residual learning (RL) technique to resolve the long-term dependency problem and transforms obtained low-frequency features into high-frequency features. The HFFEB exploits low- and high-frequency features to obtain more robust super-resolution features and address excessive feature enhancement problem. Addditionally, it also takes charge of reconstructing a high-resolution (HR) image. Extensive experiments show that our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems. The code of the ACNet is shown at https://github.com/hellloxiaotian/ACNet.

• The paper introduces ACNet, a novel CNN architecture that uses asymmetric, memory, and high-frequency feature blocks for enhanced image reconstruction.
• ACNet employs an Asymmetric Block to capture directional features, a Memory Enhancement Block for fusing hierarchical details, and a High-Frequency Feature Enhancement Block for balanced enhancement.
• Experimental results demonstrate ACNet's superior PSNR and SSIM metrics over state-of-the-art methods, highlighting its computational efficiency for practical applications.

Overview of Asymmetric CNN for Image Super-Resolution

The paper "Asymmetric CNN for Image Super-Resolution" introduces a novel convolutional neural network (CNN) architecture, referred to as ACNet, designed specifically for the task of single image super-resolution (SISR). The architecture innovatively leverages asymmetric convolutions to address common inefficiencies found in conventional super-resolution models.

Key Contributions and Methodology

1. Asymmetric Convolutional Architecture (ACNet):
   • The ACNet architecture is composed of three primary components: an Asymmetric Block (AB), a Memory Enhancement Block (MEB), and a High-Frequency Feature Enhancement Block (HFFEB). These components interact to enhance the extraction and reconstruction of high-resolution images from low-resolution inputs.
2. Asymmetric Block (AB):
   • This component introduces asymmetric convolutions, employing one-dimensional filters to boost vital local feature points in the horizontal and vertical directions. The approach aims to improve model effectiveness while avoiding redundancy present in traditional square convolutional kernels.
3. Memory Enhancement Block (MEB):
   • The MEB fuses hierarchical features extracted from previous layers using residual learning techniques. It addresses potential long-term dependency issues within deep networks and converts low-frequency information into high-frequency features crucial for high-quality image reconstruction.
4. High-Frequency Feature Enhancement Block (HFFEB):
   • This block integrates low- and high-frequency features to ensure robust super-resolution feature extraction. A key task of the HFFEB is to mitigate any surplus enhancement, providing a balance necessary for sharp and accurate image restoration.

Experimental Results

The research offers extensive experimental validation of ACNet's capabilities, comparing it with over twenty state-of-the-art methods across multiple public benchmark datasets, including Set5, Set14, B100, and Urban100, with varying scale factors. ACNet consistently demonstrates superior or comparable results in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, indicating effective image restoration. Furthermore, ACNet shows efficient computational performance, highlighting its potential for practical deployment in real-world scenarios, such as mobile devices where computational resources are constrained.

Implications and Future Directions

The introduction of an asymmetric CNN framework in SISR reveals substantial performance gains and computational efficiency, indicating a promising direction for further exploration in low-level vision tasks. The paper suggests that the principles of asymmetric convolutions could be extended to other image enhancement domains, potentially influencing future designs in machine learning applications focusing on resource efficiency. Moreover, the adept handling of multiple degradations, including different noise levels, proposes an adaptable framework that could lead to universally robust models for image restoration tasks.

In conclusion, the "Asymmetric CNN for Image Super-Resolution" paper significantly contributes to the field by introducing efficient convolutional strategies that enhance performance in image reconstruction while managing computational costs. The proposed ACNet architecture sets a precedent for adopting asymmetric processing mechanisms in developing advanced solutions for super-resolution and related image processing challenges.