Frequency Separation Network for Image Super-Resolution

Li, Shanshan; Cai, Qiang; Li, Haisheng; Cao, Jian; Wang, Lei; Li, Zhuangzi

doi:10.1109/access.2020.2972927

Cited by 13 publications

(13 citation statements)

References 41 publications

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“…We mainly compare our model with several classical and recent works, including SRCNN [3], FSRCNN [29], VDSR [30], DRCN [31], LapSRN [8,9], RAN [38], DNCL [51], FilterNet [37], MRFN [39], SeaNet [11], DEGREE [13], IRLP [40], FSN [52], and DSRLN [53]. Table 3 shows the PSNR/SSIM performance on five benchmarks with scaling factor ×2, ×3, and ×4.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Multilevel Feature Exploration Network for Image Superresolution

Liu

Wang²,

Chen

et al. 2022

Scientific Programming

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Image superresolution (SR) is a classical issue in computer vision area. Recently, there are elaborated convolutional neural networks (CNNs) demonstrating remarkable effectiveness on image SR. However, most of the previous works lack effective exploration on the structural information, which plays a critical role for image quality. In this paper, we find that the hierarchical design can effectively restore the structural information and devise a multilevel feature exploration network for image SR (MFSR). Specially, we design an encoder-decoder architecture to concentrate on structural information from different levels and devise a spatial attention mechanism to address the inherent correlation among features for effective restoration. Experimental results show the proposed MFSR can restore more correct edges and lines and achieves both better objective and subjective performances than the state-of-the-art methods with higher PSNR/SSIM results, indicating the effectiveness on structural information restoration.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Multilevel Feature Exploration Network for Image Superresolution

Liu

Wang²,

Chen

et al. 2022

Scientific Programming

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“…In these methods, one branch is responsible for capturing high-frequency features such as texture and edge, and another is to learn low-frequency features such as image outline and contour. Similarly, Li et al [33] introduced the octave convolution to image SR which uses two branches to perform information update and frequency communication between low-and high-frequency features.…”

Section: B Frequency Based Sr Methodsmentioning

confidence: 99%

“…However, as SR networks are so diverse, the attention module is usually designed solely for a specific network structure [55]. Recently, various SR methods such as multi-branch networks [33,60] and progressive reconstruction methods [35,69] mainly focus on refining the highfrequency texture details. Although these methods delivered impressive results, they demand substantial memory and computational resources.…”

Section: Introductionmentioning

confidence: 99%

Frequency-Based Enhancement Network for Efficient Super-Resolution

Behjati¹,

Rodríguez

Fernández³

et al. 2022

IEEE Access

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Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of highfrequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low-and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model -Frequency-based Enhancement Network (FENet) -based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-ofthe-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet.

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“…To show the performance of our LandNet, we compare our network with several traditional and recent works: SRCNN [13], FSRCNN [29], VDSR [14], DRCN [58], LapSRN [59], SelNet [60], RAN [61], DNCL [62], FilterNet [63], MRFN [64], SeaNet [65], DEGREE [66], FSN [67], MFSR [68], DSRLN [69], and MemNet [56]. e indicators are chosen as peak signal-tonoise ratio (PSNR) and structural similarity (SSIM).…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Landweber Iteration-Inspired Network for Image Super-Resolution

Wang

Liu

2022

Mathematical Problems in Engineering

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Image super-resolution (SR) is one of the classical ill-posed image processing issues to generate high-resolution (HR) images from given low-resolution (LR) instances. Recent SR works aim to find an elaborate convolutional neural network (CNN) design and regard it as an end-to-end filter to map the image from LR space to HR space. However, seldom of them concentrate on the mathematical proof of network design or consider the problem from an optimization perspective. In this paper, we investigate the image SR based on the Landweber iteration method, which is an effective optimization method to find a feasible solution for the ill-posed problem. By considering the issue from the optimization perspective, we design a corresponding Landweber iteration-inspired network to adaptively learn the parameters and find the HR results. Experimental results show the proposed network achieves competitive or better subjective and objective performance than other state-of-the-art methods with fewer parameters and computational costs.

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Frequency Separation Network for Image Super-Resolution

Cited by 13 publications

References 41 publications

Multilevel Feature Exploration Network for Image Superresolution

Multilevel Feature Exploration Network for Image Superresolution

Frequency-Based Enhancement Network for Efficient Super-Resolution

Landweber Iteration-Inspired Network for Image Super-Resolution

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