With the successful development of deep learning, single image super-resolution (SISR) has advanced significantly in recent years. However, in practice, excessive convolutions limit super-resolution applications on platforms with limited resources like mobile devices or embedded systems. Besides, existing lightweight models have a problem with small receptive fields and only consider local features for the reconstruction. Previous models try to stack more convolutions layers to address this problem, but this prolongs the execution time due to increasing the number of parameters. Therefore, this paper proposes a novel approach named F2SRGAN using a revised Fast Fourier Convolution to enlarge the receptive field, enabling this model to learn global features better, neither introducing too many parameters nor prolonging the model inference time. The experimental results show that our proposed F2SRGAN significantly improves perceptual image quality among the lightweight SISR methods while maintaining an acceptable inference time.INDEX TERMS Depth-wise separable convolution (DSC), Fast Fourier convolution (FFC), Perceptual index (PI), Single image super-resolution (SISR)
Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight Generative Adversarial Network (GAN)-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.
Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight GAN-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.
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