Original: ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
Revised: ShuffleMixer: An Efficient ConvNet for Image Super-Resolution

Original: Abstract
Revised: Abstract

Original: Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms.
Revised: Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms.

Original: We propose a simple and effective approach,
Revised: We propose a simple and effective approach,

Original: ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation.
Revised: ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation.

Original: In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design.
Revised: In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design.

Original: Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently.
Revised: Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently.

Original: Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details.
Revised: Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details.

Original: To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features.
Revised: To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features.

Original: Experimental results demonstrate that the proposed ShuffleMixer is about 3 × smaller than the state-of-the-art efficient SR methods, e.g. CARN, in terms of model parameters and FLOPs while achieving competitive performance.
Revised: Experimental results demonstrate that the proposed ShuffleMixer is about 3 × smaller than the state-of-the-art efficient SR methods, e.g. CARN, in terms of model parameters and FLOPs while achieving competitive performance.

Original: The code is available at https://github.com/sunny2109/ShuffleMixer .
Revised: The code is available at https://github.com/sunny2109/ShuffleMixer .

Original:  Introduction
Revised:  Introduction

Original: Single image super-resolution (SISR) aims to recover a high-resolution image from a low-resolution one.
Revised: Single image super-resolution (SISR) aims to recover a high-resolution image from a low-resolution one.

Original: This is a classical problem that has attracted lots of attention recently due to the rapid development of high-definition devices, such as Ultra-High Definition Television, Samsung Galaxy S
Revised: This is a classical problem that has attracted lots of attention recently due to the rapid development of high-definition devices, such as Ultra-High Definition Television, Samsung Galaxy S

Original: Ultra, iPhone 13 Pro Max and HUAWEI P50 Pro, and so on.
Revised: Ultra, iPhone 13 Pro Max and HUAWEI P50 Pro, and so on.

Original: Thus, it is of great interest to develop an efficient and effective method to estimate high-resolution images to be better displayed on these devices.
Revised: Thus, it is of great interest to develop an efficient and effective method to estimate high-resolution images to be better displayed on these devices.

Original: Recently, convolutional neural network (CNN) based SR models [8, 9, 1, 16, 25, 45] have achieved impressive reconstruction performance.
Revised: Recently, convolutional neural network (CNN) based SR models [8, 9, 1, 16, 25, 45] have achieved impressive reconstruction performance.

Original: However, these networks hierarchically extract local features, which highly rely on stacking deeper or more complex models to enlarge the receptive fields for performance improvements.
Revised: However, these networks hierarchically extract local features, which highly rely on stacking deeper or more complex models to enlarge the receptive fields for performance improvements.

Original: As a result, the required computational budget makes these heavy SR models challenging to deploy on resource-constrained mobile devices in practical applications [44].
Revised: As a result, the required computational budget makes these heavy SR models challenging to deploy on resource-constrained mobile devices in practical applications [44].

Original: To alleviate heavy SR models, various methods have been proposed to reduce model complexity or speed up runtime, including efficient operation design [32, 28, 36, 9, 16, 1, 33, 43, 23, 27], neural architecture search [6, 35], knowledge distillation [12, 13], and structural re-parameterization methodology [7, 23, 44].
Revised: To alleviate heavy SR models, various methods have been proposed to reduce model complexity or speed up runtime, including efficient operation design [32, 28, 36, 9, 16, 1, 33, 43, 23, 27], neural architecture search [6, 35], knowledge distillation [12, 13], and structural re-parameterization methodology [7, 23, 44].

Original: These methods are mainly based on improved small spatial convolutions or advanced training strategies, and large kernel convolutions are rarely explored.
Revised: These methods are mainly based on improved small spatial convolutions or advanced training strategies, and large kernel convolutions are rarely explored.

Original: Moreover, they mostly focus on one of the efficiency indicators and do not perform well in real resource-constrained tasks.
Revised: Moreover, they mostly focus on one of the efficiency indicators and do not perform well in real resource-constrained tasks.

Original: Thus, the demand for a better trade-off between complexity, latency, and SR quality is imperative.
Revised: Thus, the demand for a better trade-off between complexity, latency, and SR quality is imperative.

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: CARN
Revised: CARN

Original: EDSR-baseline
Revised: EDSR-baseline

Original: LAPAR-A
Revised: LAPAR-A

Original: [Figure]
Revised: [Figure]

Original: A large receptive field involves more feature interactions, which helps reconstruct more refined results in tasks such as super-resolution that require dense per-pixel predictions.
Revised: A large receptive field involves more feature interactions, which helps reconstruct more refined results in tasks such as super-resolution that require dense per-pixel predictions.

Original: Recent visual transformer (ViT)-based approaches [10, 26, 30, 24] employ a multi-head self-attention (MHSA) mechanism to learn long-range feature representations, which leads to their state-of-the-art performance in various vision tasks.
Revised: Recent visual transformer (ViT)-based approaches [10, 26, 30, 24] employ a multi-head self-attention (MHSA) mechanism to learn long-range feature representations, which leads to their state-of-the-art performance in various vision tasks.

Original: However, MHSA is not friendly to enlarging the receptive field of an efficient SR design.
Revised: However, MHSA is not friendly to enlarging the receptive field of an efficient SR design.

Original: Its complexity grows quadratically with the input resolution (the size is usually large and constant during SR training).
Revised: Its complexity grows quadratically with the input resolution (the size is usually large and constant during SR training).

Original: Regular convolution with large kernels is also a simple but heavyweight approach to obtaining efficient receptive fields.
Revised: Regular convolution with large kernels is also a simple but heavyweight approach to obtaining efficient receptive fields.

Original: To make large kernel convolutions practical, using depth-wise convolutions with large kernel sizes [27, 39, 7] is an effective alternative.
Revised: To make large kernel convolutions practical, using depth-wise convolutions with large kernel sizes [27, 39, 7] is an effective alternative.

Original: Since depth-wise convolutions share connection weights between spatial locations and remain independent between channels, this property makes it challenging to capture sufficient interactions.
Revised: Since depth-wise convolutions share connection weights between spatial locations and remain independent between channels, this property makes it challenging to capture sufficient interactions.

Original: It is essential to improve the learning capability of depth-wise convolutions (DW Convs) in lightweight network design.
Revised: It is essential to improve the learning capability of depth-wise convolutions (DW Convs) in lightweight network design.

Original: In this paper, we develop a simple and effective network named ShuffleMixer that introduces large kernel convolutions for lightweight SR design.
Revised: In this paper, we develop a simple and effective network named ShuffleMixer that introduces large kernel convolutions for lightweight SR design.

Original: The core idea is to fuse non-local and local spatial locations within a feature mixing block with fewer parameters and FLOPs.
Revised: The core idea is to fuse non-local and local spatial locations within a feature mixing block with fewer parameters and FLOPs.

Original: Specifically, we employ depth-wise convolutions with large kernel sizes to aggregate spatial information from a large region.
Revised: Specifically, we employ depth-wise convolutions with large kernel sizes to aggregate spatial information from a large region.

Original: For channel mixing, we introduce channel splitting and shuffling strategies to reduce model parameters and computational costs and improve network capability.
Revised: For channel mixing, we introduce channel splitting and shuffling strategies to reduce model parameters and computational costs and improve network capability.

Original: We then build an effective shuffle mixer layer based on these two operators.
Revised: We then build an effective shuffle mixer layer based on these two operators.

Original: To further improve the learning capability, we embed the Fused-MBConv into the mixer layer to boost local connectivity.
Revised: To further improve the learning capability, we embed the Fused-MBConv into the mixer layer to boost local connectivity.

Original: Taken together, we find that the ShuffleMixer network with a simple module can obtain state-of-the-art performance.
Revised: Taken together, we find that the ShuffleMixer network with a simple module can obtain state-of-the-art performance.

Original: Figureshows that our ShuffleMixer achieves a better trade-off with the least parameters and FLOPs among all existing lightweight SR methods.
Revised: Figureshows that our ShuffleMixer achieves a better trade-off with the least parameters and FLOPs among all existing lightweight SR methods.

Original: The contributions of this paper are summarized as follows: (1) We develop an efficient SR design by exploring a large kernel ConvNet that involves more useful information for image SR.
Revised: The contributions of this paper are summarized as follows: (1) We develop an efficient SR design by exploring a large kernel ConvNet that involves more useful information for image SR.

Original: (2) We introduce a channel splitting and shuffling operation to perform feature mixing of the channel projection efficiently.
Revised: (2) We introduce a channel splitting and shuffling operation to perform feature mixing of the channel projection efficiently.

Original: (3) To better explore the local connectivity among cross-group features from the shuffle mixer layer, we utilize Fused-MBConvs in the proposed SR design.
Revised: (3) To better explore the local connectivity among cross-group features from the shuffle mixer layer, we utilize Fused-MBConvs in the proposed SR design.

Original: We formulate the aforementioned modules into an end-to-end trainable network, which is named as ShuffleMixer.
Revised: We formulate the aforementioned modules into an end-to-end trainable network, which is named as ShuffleMixer.

Original: Experimental results show that ShuffleMixer is about 3 × smaller than the state-of-the-art methods in terms of model parameters and FLOPs while achieving competitive performance compared to the state-of-the-art methods.
Revised: Experimental results show that ShuffleMixer is about 3 × smaller than the state-of-the-art methods in terms of model parameters and FLOPs while achieving competitive performance compared to the state-of-the-art methods.

Original:  Related Work
Revised:  Related Work

Original: CNN-based Efficient SR.
Revised: CNN-based Efficient SR.

Original: CNN-based methods adopt various ways to reduce model complexity.
Revised: CNN-based methods adopt various ways to reduce model complexity.

Original: FSRCNN [9] and ESPCN [33] employ post-upsampling layers to significantly reduce the compu- tational burden from predefined inputs.
Revised: FSRCNN [9] and ESPCN [33] employ post-upsampling layers to significantly reduce the compu- tational burden from predefined inputs.

Original: Namhyuk et al.
Revised: Namhyuk et al.

Original: [1] uses group convolution and cascading connection upon a recursive network to save parameters.
Revised: [1] uses group convolution and cascading connection upon a recursive network to save parameters.

Original: Hui et al.
Revised: Hui et al.

Original: [16] proposes a lightweight information multi-distillation network (IMDN) to aggregate features by applying feature splitting and concatenation operations, and the improved IMDN variants [43, 23] won the AIM2020 and
Revised: [16] proposes a lightweight information multi-distillation network (IMDN) to aggregate features by applying feature splitting and concatenation operations, and the improved IMDN variants [43, 23] won the AIM2020 and

Original: NTIRE

Revised: NTIRE

Original: Efficient SR challenge.
Revised: Efficient SR challenge.

Original: Meanwhile, an increasingly popular approach is to search for a well-constrained architecture as a multi-objective evolution problem [6, 35].
Revised: Meanwhile, an increasingly popular approach is to search for a well-constrained architecture as a multi-objective evolution problem [6, 35].

Original: Another branch compresses and accelerates a heavy deep model through knowledge distillation [13, 12].
Revised: Another branch compresses and accelerates a heavy deep model through knowledge distillation [13, 12].

Original: Note that fewer parameters and FLOPs do not sufficiently mean faster runtime on mobile devices because
Revised: Note that fewer parameters and FLOPs do not sufficiently mean faster runtime on mobile devices because

Original: FLOPs ignore important latency-related factors such as memory access cost (MAC) and degree of parallelism [28, 32].
Revised: FLOPs ignore important latency-related factors such as memory access cost (MAC) and degree of parallelism [28, 32].

Original: In this paper, we analyze factors affecting the efficiency of SR models and develop a mobile-friendly SR network.
Revised: In this paper, we analyze factors affecting the efficiency of SR models and develop a mobile-friendly SR network.

Original: Transformer-based SR.
Revised: Transformer-based SR.

Original: Transformers were initially proposed for language tasks, which stacked the multi-head self-attention and feed-forward MLP layers to learn long-range relations among its inputs.
Revised: Transformers were initially proposed for language tasks, which stacked the multi-head self-attention and feed-forward MLP layers to learn long-range relations among its inputs.

Original: Dosovitskiy et al.
Revised: Dosovitskiy et al.

Original: [10] first applied a vision transformer to image recognition.
Revised: [10] first applied a vision transformer to image recognition.

Original: Since then,
Revised: Since then,

Original: ViT-based models have become increasingly applicable to both high-level and low-level vision tasks.
Revised: ViT-based models have become increasingly applicable to both high-level and low-level vision tasks.

Original: For image super-resolution, Chen et al.
Revised: For image super-resolution, Chen et al.

Original: [4] develop a pre-trained image processing transformer (IPT) that directly applies the vanilla ViT to non-overlapped patch embeddings.
Revised: [4] develop a pre-trained image processing transformer (IPT) that directly applies the vanilla ViT to non-overlapped patch embeddings.

Original: Liang et al.
Revised: Liang et al.

Original: [24] follow
Revised: [24] follow

Original: Swin Transformer [26] and propose a window-based self-attention model for image restoration tasks and achieve excellent results.
Revised: Swin Transformer [26] and propose a window-based self-attention model for image restoration tasks and achieve excellent results.

Original: Window-based self-attention is much more computationally efficient than global self-attention, but it is still a time-consuming and memory-intensive operation.
Revised: Window-based self-attention is much more computationally efficient than global self-attention, but it is still a time-consuming and memory-intensive operation.

Original: Models with Large Kernels.
Revised: Models with Large Kernels.

Original: AlexNet [20] is a classic large-kernel convolutional neural network model that inspired many subsequent works.
Revised: AlexNet [20] is a classic large-kernel convolutional neural network model that inspired many subsequent works.

Original: Global Convolutional Network [31] uses symmetric, separable large filters to improve semantic segmentation performance.
Revised: Global Convolutional Network [31] uses symmetric, separable large filters to improve semantic segmentation performance.

Original: Due to the high computational cost and a large number of parameters, large-size convolutional filters became not popular after
Revised: Due to the high computational cost and a large number of parameters, large-size convolutional filters became not popular after

Original: VGG-Net [34].
Revised: VGG-Net [34].

Original: However, large convolution kernels have recently gained attention with the development of efficient convolution techniques and new architectures such as transformers and MLPs.
Revised: However, large convolution kernels have recently gained attention with the development of efficient convolution techniques and new architectures such as transformers and MLPs.

Original: ConvMixer [39] replaces the mixer component of ViTs [26, 10] or MLPs [38] with large kernel depth-wise convolutions.
Revised: ConvMixer [39] replaces the mixer component of ViTs [26, 10] or MLPs [38] with large kernel depth-wise convolutions.

Original: ConvNeXt [27] uses 7 × 7 depth-wise kernels to redesign a standard ResNet and achieves comparable results to Transformers. RepLKNet
Revised: ConvNeXt [27] uses 7 × 7 depth-wise kernels to redesign a standard ResNet and achieves comparable results to Transformers. RepLKNet

Original: [7] enlarges the convolution kernel to 31 × 31 to build a pure CNN model, which obtains better results than Swin Transformer [26] on ImageNet.
Revised: [7] enlarges the convolution kernel to 31 × 31 to build a pure CNN model, which obtains better results than Swin Transformer [26] on ImageNet.

Original: Unlike these methods that focus on building big models for high-level vision tasks, we explore the possibility of large convolution kernels for lightweight model design in image super-resolution.
Revised: Unlike these methods that focus on building big models for high-level vision tasks, we explore the possibility of large convolution kernels for lightweight model design in image super-resolution.

Original:  Proposed Method
Revised:  Proposed Method

Original: We aim to develop an efficient large-kernel CNN model for the SISR task.
Revised: We aim to develop an efficient large-kernel CNN model for the SISR task.

Original: To this end, we introduce key designs to the feature mixing block employed to encode information efficiently.
Revised: To this end, we introduce key designs to the feature mixing block employed to encode information efficiently.

Original: In this section, we first present the overall pipeline of our proposed ShuffleMixer network.
Revised: In this section, we first present the overall pipeline of our proposed ShuffleMixer network.

Original: Then, we formulate the feature mixing block, which acts as a basic module for building the ShuffleMixer network.
Revised: Then, we formulate the feature mixing block, which acts as a basic module for building the ShuffleMixer network.

Original: Finally, we provide details on the training loss function.
Revised: Finally, we provide details on the training loss function.

Original: 3.1 ShuffleMixer Architecture
Revised: 3.1 ShuffleMixer architecture

Original: The overall ShuffleMixer architecture.
Revised: The overall ShuffleMixer architecture.

Original: Given a low-resolution image I LR ∈ R C × H × W , where C ,
Revised: Given a low-resolution image I LR ∈ R C × H × W , where C ,

Original: H × W denote the number of channels and the spatial resolution, respectively.
Revised: H × W denote the number of channels and the spatial resolution, respectively.

Original: For a color image, the value of C is 3.
Revised: For a color image, the value of C is 3.

Original: The proposed ShuffleMixer first extracts feature Z 0 ∈ R D
Revised: The proposed ShuffleMixer first extracts feature Z 0 ∈ R D

Original: × H × W by a convolution operation with a filter size of 3 × 3 pixels and D channels.
Revised: × H × W by a convolution operation with a filter size of 3 × 3 pixels and D channels.

Original: Then, we develop a feature mixing block (FMB) consisting of a Fused-MBConv [36] and two shuffle mixer layers, which takes the feature Zas input to produce a deeper feature Z 1 ∈ R D × H × W .
Revised: Then, we develop a feature mixing block (FMB) consisting of a Fused-MBConv [36] and two shuffle mixer layers, which takes the feature Zas input to produce a deeper feature Z 1 ∈ R D × H × W .

Original: Next, we utilize an upsampler module with a scale factor s to upscale the spatial resolution of the features generated by a sequence of FMBs.
Revised: Next, we utilize an upsampler module with a scale factor s to upscale the spatial resolution of the features generated by a sequence of FMBs.

Original: To save parameters of the enlargement module as much as possible, we only use a convolutional layer of size 1 × 1 and a pixel shuffling layer [33].
Revised: To save parameters of the enlargement module as much as possible, we only use a convolutional layer of size 1 × 1 and a pixel shuffling layer [33].

Original: For the × 4 scale factor, we progressively upsample the resolution by repeating the × 2 upsampler two times.
Revised: For the × 4 scale factor, we progressively upsample the resolution by repeating the × 2 upsampler two times.

Original: Finally, we use a convolutional layer to map the upscaled feature to the residual image I R ∈ R C × sH × sW , and add it to the upscaled I LR by bilinear interpolation to get the final high-resolution image I SR : I SR = ↑ s ( I LR )
Revised: Finally, we use a convolutional layer to map the upscaled feature to the residual image I R ∈ R C × sH × sW , and add it to the upscaled I LR by bilinear interpolation to get the final high-resolution image I SR : I SR = ↑ s ( I LR )

Original: + I R , where ↑ s ( · ) denotes the bilinear interpolation with scale factor s .
Revised: + I R , where ↑ s ( · ) denotes the bilinear interpolation with scale factor s .

Original: In the following, we explain the proposed method in detail.
Revised: In the following, we explain the proposed method in detail.

Original: C o n v 3 ×
Revised: C o n v 3 ×

Original: S hu
Revised: S hu

Original: ff l e
Revised: ff l e

Original: M i x e r
Revised: M i x e r

Original: L a y e r
Revised: L a y e r

Original: S hu
Revised: S hu

Original: ff l e
Revised: ff l e

Original: M i x e r
Revised: M i x e r

Original: L a y e r
Revised: L a y e r

Original: F u s e dM BC onv
Revised: F u s e dM BC onv

Original: Feature Mixing Block
Revised: Feature Mixing Block

Original: F e a t u re
Revised: F e a t u re

Original: M i x i n g
Revised: M i x i n g

Original: B l o c k
Revised: B l o c k

Original: F e a t u re
Revised: F e a t u re

Original: M i x i n g
Revised: M i x i n g

Original: B l o c k … …
Revised: B l o c k … …

Original: C o n v 3 ×
Revised: C o n v 3 ×

Original: U p s a m p l er
Revised: U p s a m p l er

Original: Bilinear Upsampling (a)
Revised: Bilinear Upsampling (a)

Original: [Figure]
Revised: [Figure]

Original: Depthwise Convolution
Revised: Depthwise Convolution

Original: [Figure]
Revised: [Figure]

Original: Point-wise MLP
Revised: Point-wise MLP

Original: Conv 1 ×
Revised: Conv 1 ×

Original: SiLU
Revised: SiLU

Original: Conv 1 ×
Revised: Conv 1 ×

Original: C, H, W 2 C, H, W
Revised: C, H, W 2 C, H, W

Original: C, H, W (d)
Revised: C, H, W (d)

Original: Fused-MBConv
Revised: Fused-MBConv

Original: Conv 3 ×
Revised: Conv 3 ×

Original: SiLU
Revised: SiLU

Original: Conv 1 ×
Revised: Conv 1 ×

Original: C, H, W
Revised: C, H, W

Original: C+ 𝐶 ′ , H, W
Revised: C+ 𝐶 ′ , H, W

Original: C, H, W (e)
Revised: C, H, W (e)

Original: L a y e r N o r m
Revised: L a y e r N o r m

Original: C h a nn e l
Revised: C h a nn e l

Original: S p litti ng 𝑍
Revised: S p litti ng 𝑍

Original: P o i n t - w i s e
Revised: P o i n t - w i s e

Original: M L P
Revised: M L P

Original: Identity
Revised: Identity

Original: C h a nn e l
Revised: C h a nn e l

Original: S hu
Revised: S hu

Original: ff li ng
Revised: ff li ng

Original: Channel Projection
Revised: Channel Projection

Original: Skip-connection (c) 𝑍
Revised: Skip-connection (c) 𝑍

Original: [Figure]
Revised: [Figure]

Original: The Feature Mixing Block is developed to explore local and non-local information for better feature representations.
Revised: The Feature Mixing Block is developed to explore local and non-local information for better feature representations.

Original: To obtain non-local feature interactions effectively, we apply shuffle mixer layers on Z 0 , as illustrated in Figure 2(a).
Revised: To obtain non-local feature interactions effectively, we apply shuffle mixer layers on Z 0 , as illustrated in Figure 2(a).

Original: For each shuffle mixer layer, we employ a large kernel DW
Revised: For each shuffle mixer layer, we employ a large kernel DW

Original: Conv to mix features at spatial locations.
Revised: Conv to mix features at spatial locations.

Original: This operation enjoys large receptive fields with fewer parameters, which can encode more spatial information for reconstructing complete and accurate structures.
Revised: This operation enjoys large receptive fields with fewer parameters, which can encode more spatial information for reconstructing complete and accurate structures.

Original: As we investigated in Table 3, depth-wise convolutions with larger sizes consistently improve SR performance while maintaining computational efficiency.
Revised: As we investigated in Table 3, depth-wise convolutions with larger sizes consistently improve SR performance while maintaining computational efficiency.

Original: To mix features at channel locations, we employ point-wise MLPs to perform channel projection.
Revised: To mix features at channel locations, we employ point-wise MLPs to perform channel projection.

Original: With the help of depth-wise convolution, the computational cost of the shuffle mixer layer is mainly caused by channel projections.
Revised: With the help of depth-wise convolution, the computational cost of the shuffle mixer layer is mainly caused by channel projections.

Original: We further introduce a channel splitting and shuffling (CSS) strategy [28] to improve the efficiency of this step.
Revised: We further introduce a channel splitting and shuffling (CSS) strategy [28] to improve the efficiency of this step.

Original: Specifically, the input feature Z 0 is first to split into Z 10 and
Revised: Specifically, the input feature Z 0 is first to split into Z 10 and

Original: Z 20 ; then, a point-wise MLP performs channel mixing on the split feature Z 10 ; finally, a channel shuffling operation is employed to enable the exchange of information on the concatenate feature.
Revised: Z 20 ; then, a point-wise MLP performs channel mixing on the split feature Z 10 ; finally, a channel shuffling operation is employed to enable the exchange of information on the concatenate feature.

Original: Therefore, the parameter complexity of the channel projection layer drops from Ω(4 C 2 ) to Ω( C 2 ) .
Revised: Therefore, the parameter complexity of the channel projection layer drops from Ω(4 C 2 ) to Ω( C 2 ) .

Original: This procedure can be formulated as follow:
Revised: This procedure can be formulated as follow:

Original: [Equation]
Revised: [Equation]

Original: where σ is SiLU nonlinearity function [11], W 0 and W 1 are the point-wise convolutions; Split ( · ) and
Revised: where σ is SiLU nonlinearity function [11], W 0 and W 1 are the point-wise convolutions; Split ( · ) and

Original: Shuffle ( · ) represent the splitting and shuffling of features in the channel dimension.
Revised: Shuffle ( · ) represent the splitting and shuffling of features in the channel dimension.

Original: This splitting operation limits representational capability since we exclude the other half of the input tensors from channel interactions.
Revised: This splitting operation limits representational capability since we exclude the other half of the input tensors from channel interactions.

Original: The channel shuffle operation cannot guarantee that all features are processed.
Revised: The channel shuffle operation cannot guarantee that all features are processed.

Original: Inspired by the MobileNetv2 block [32], we thus repeat the channel projection layer and arrange them before and after the large depth-wise convolution to learn visual representations.
Revised: Inspired by the MobileNetv2 block [32], we thus repeat the channel projection layer and arrange them before and after the large depth-wise convolution to learn visual representations.

Original: From our study, as listed in Table 2, the enhanced mixer layer achieves quite similar results to the ConvMixer block [39] while using fewer parameters and FLOPs.
Revised: From our study, as listed in Table 2, the enhanced mixer layer achieves quite similar results to the ConvMixer block [39] while using fewer parameters and FLOPs.

Original: Since the content of natural images is locally correlated, the stacked Shuffle Mixer Layers do not fully exploit local features.
Revised: Since the content of natural images is locally correlated, the stacked Shuffle Mixer Layers do not fully exploit local features.

Original: It requires more capacity to model feature representations for better SR performance.
Revised: It requires more capacity to model feature representations for better SR performance.

Original: Therefore, we embed a few convolutional blocks into the proposed model to enhance local connectivity.
Revised: Therefore, we embed a few convolutional blocks into the proposed model to enhance local connectivity.

Original: Concretely, we evenly add the Fused-MBConv after every two shuffle mixer layers.
Revised: Concretely, we evenly add the Fused-MBConv after every two shuffle mixer layers.

Original: The original Fused-MBConv contains an expansion convolution of size 3 × 3 , an SE layer [14] (i.e., the commonly used channel attention), and a reduction convolution of size 1 × 1 .
Revised: The original Fused-MBConv contains an expansion convolution of size 3 × 3 , an SE layer [14] (i.e., the commonly used channel attention), and a reduction convolution of size 1 × 1 .

Original: Using such a
Revised: Using such a

Original: Fused-MBConv significantly increases parameters and FLOPs, which motivates us to make some changes to match the computational requirements.
Revised: Fused-MBConv significantly increases parameters and FLOPs, which motivates us to make some changes to match the computational requirements.

Original: We remove the SE layer first, as the SiLU function can be somewhat treated as a gating mechanism.
Revised: We remove the SE layer first, as the SiLU function can be somewhat treated as a gating mechanism.

Original: Note that the inference time is much slower as the hidden dimension expands.
Revised: Note that the inference time is much slower as the hidden dimension expands.

Original: Instead of expanding the hidden channel rapidly with a large factor (where the default expansion factor is usually set to be 6) of this expansion convolution, we then limit the number of output channels and expand it to C + C ′ ( C ′ is experimentally set to 16), as shown in
Revised: Instead of expanding the hidden channel rapidly with a large factor (where the default expansion factor is usually set to be 6) of this expansion convolution, we then limit the number of output channels and expand it to C + C ′ ( C ′ is experimentally set to 16), as shown in

Original: Figure 2(d).
Revised: Figure 2(d).

Original: We also study several operations for this mixing process, and more details are included in Sec. 4.3.
Revised: We also study several operations for this mixing process, and more details are included in Sec. 4.3.

Original: 3.2 Learning Strategy
Revised: 3.2 Learning strategy

Original: To constrain the network training, a straightforward way is to ensure that the content of the network output is close to that of the ground truth image:
Revised: To constrain the network training, a straightforward way is to ensure that the content of the network output is close to that of the ground truth image:

Original: [Equation]
Revised: [Equation]

Original: where I SR and I GT denote the network output and the corresponding ground truth HR image.
Revised: where I SR and I GT denote the network output and the corresponding ground truth HR image.

Original: We note that only using the above pixel-wise loss function does not effectively help high-frequency details estimation [5].
Revised: We note that only using the above pixel-wise loss function does not effectively help high-frequency details estimation [5].

Original: We accordingly employ a frequency constraint to regularize network training, where the final loss function is defined as:
Revised: We accordingly employ a frequency constraint to regularize network training, where the final loss function is defined as:

Original: [Equation]
Revised: [Equation]

Original: where F denotes the Fast Fourier transform, and λ is a weight parameter that is set to be 0.
Revised: where F denotes the Fast Fourier transform, and λ is a weight parameter that is set to be 0.

Original: empirically.
Revised: empirically.

Original:  Experimental Results 4.1 Datasets and implementation
Revised:  Experimental Results 4.1 Datasets and implementation

Original: Datasets.
Revised: Datasets.

Original: Following existing methods [22, 24, 23], we train our models on the DF2K dataset, a merged dataset with DIV2K [37] and Flickr2K [25], which contains 3450 (800 + 2650) high-quality images.
Revised: Following existing methods [22, 24, 23], we train our models on the DF2K dataset, a merged dataset with DIV2K [37] and Flickr2K [25], which contains 3450 (800 + 2650) high-quality images.

Original: We adopt standard protocols to generate LR images by bicubic downscaling of reference HR images.
Revised: We adopt standard protocols to generate LR images by bicubic downscaling of reference HR images.

Original: During the testing stage, we evaluate our models with the peak signal to noise ratio (PSNR) and the structural similarity index (SSIM) on five publicly available benchmark datasets - Set5 [3],
Revised: During the testing stage, we evaluate our models with the peak signal to noise ratio (PSNR) and the structural similarity index (SSIM) on five publicly available benchmark datasets - Set5 [3],

Original: Set14 [42], B100 [2], Urban100 [15] and Manga109 [29].
Revised: Set14 [42], B100 [2], Urban100 [15] and Manga109 [29].

Original: All PSNR and SSIM results are calculated on the Y channel from the YCbCr color space.
Revised: All PSNR and SSIM results are calculated on the Y channel from the YCbCr color space.

Original: Implementation details.
Revised: Implementation details.

Original: We train our model in RGB channels and augment the input patches with random horizontal flips and rotations.
Revised: We train our model in RGB channels and augment the input patches with random horizontal flips and rotations.

Original: In each training mini-batch, we randomly crop 64 patches of size 64 × 64 from LR images as the input.
Revised: In each training mini-batch, we randomly crop 64 patches of size 64 × 64 from LR images as the input.

Original: The proposed model is trained by minimizing L1 loss and the frequency loss [5] with Adam [19] optimizer for 300,000 total iterations.
Revised: The proposed model is trained by minimizing L1 loss and the frequency loss [5] with Adam [19] optimizer for 300,000 total iterations.

Original: The learning rate is set to a constant 5 × 10 − 4 .
Revised: The learning rate is set to a constant 5 × 10 − 4 .

Original: All experiments are conducted with the PyTorch framework on an Nvidia
Revised: All experiments are conducted with the PyTorch framework on an Nvidia

Original: Tesla V100 GPU.
Revised: Tesla V100 GPU.

Original: We provide two models according to the number of feature channels and DW Conv kernel size, and the number of FMB modules is 5.
Revised: We provide two models according to the number of feature channels and DW Conv kernel size, and the number of FMB modules is 5.

Original: The number of channels and convolution kernel sizes is 64 and 7 × 7 pixels for the ShuffleMixer model and 32 and 3 × 3 pixels for the ShuffleMixer-Tiny model.
Revised: The number of channels and convolution kernel sizes is 64 and 7 × 7 pixels for the ShuffleMixer model and 32 and 3 × 3 pixels for the ShuffleMixer-Tiny model.

Original: 4.2 Comparisons with State-of-the-Art Methods
Revised: 4.2 Comparisons with state-of-the-art methods

Original: To evaluate the performance of our approach, we compare the proposed ShuffleMixer with stateof-the-art lightweight algorithms, including SRCNN [8], FSRCNN [9], ESPCN [33], VDSR [18],
Revised: To evaluate the performance of our approach, we compare the proposed ShuffleMixer with stateof-the-art lightweight algorithms, including SRCNN [8], FSRCNN [9], ESPCN [33], VDSR [18],

Original: DRCN [17], LapSRN [21], CARN [1], EDSR-baseline [25], FALSR-A [6], IMDN [16], LAPAR [22],
Revised: DRCN [17], LapSRN [21], CARN [1], EDSR-baseline [25], FALSR-A [6], IMDN [16], LAPAR [22],

Original: ECBSR [44], and SMSR [40].
Revised: ECBSR [44], and SMSR [40].

Original: [Table]
Revised: [Table]

Original: Table 1 shows quantitative comparisons on benchmark datasets for the upscaling factors of × 2 , × 3 , and × 4 .
Revised: Table 1 shows quantitative comparisons on benchmark datasets for the upscaling factors of × 2 , × 3 , and × 4 .

Original: In addition to PSNR/SSIM metrics, we list the number of parameters and FLOPs.
Revised: In addition to PSNR/SSIM metrics, we list the number of parameters and FLOPs.

Original: The number of FLOPs is tested under a setting of super-resolving an image to 1280 × 720 pixels.
Revised: The number of FLOPs is tested under a setting of super-resolving an image to 1280 × 720 pixels.

Original: In
Revised: In

Original: Figure 1, we compare FLOPs and the number of parameters on the × 4 B100 dataset.
Revised: Figure 1, we compare FLOPs and the number of parameters on the × 4 B100 dataset.

Original: Here, our
Revised: Here, our

Original: ShuffleMixer model obtains competitive results with even fewer parameters and FLOPs.
Revised: ShuffleMixer model obtains competitive results with even fewer parameters and FLOPs.

Original: Especially,
Revised: Especially,

Original: ShuffleMixer has a similar number of parameters to CARN-M, but our model outperforms it by a large margin on all benchmark datasets.
Revised: ShuffleMixer has a similar number of parameters to CARN-M, but our model outperforms it by a large margin on all benchmark datasets.

Original: Even with only 113K parameters, ShuffleMixer-Tiny performs better than many existing methods.
Revised: Even with only 113K parameters, ShuffleMixer-Tiny performs better than many existing methods.

Original: With regard to the scale factor × 2 and × 3 , the proposed
Revised: With regard to the scale factor × 2 and × 3 , the proposed

Original: ShuffleMixer family can achieve similar performance.
Revised: ShuffleMixer family can achieve similar performance.

Original: Although IMDN [16], LAPAR-A [22] and SMSR [40] obtain comparable PSNR/SSIM performance,
Revised: Although IMDN [16], LAPAR-A [22] and SMSR [40] obtain comparable PSNR/SSIM performance,

Original: ShuffleMixer requires only a relatively small amount of model complexity.
Revised: ShuffleMixer requires only a relatively small amount of model complexity.

Original: Meanwhile, we compare
Revised: Meanwhile, we compare

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: ) of the proposed tiny model.
Revised: ) of the proposed tiny model.

Original: All the methods are evaluated on × 4 DIV2K validation set.
Revised: All the methods are evaluated on × 4 DIV2K validation set.

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: the GPU run time with fast and lightweight models on × 4 SR: CARN [1], CARN-M [1] and
Revised: the GPU run time with fast and lightweight models on × 4 SR: CARN [1], CARN-M [1] and

Original: LAPAR-A [22], and the proposed method has a faster inference speed.
Revised: LAPAR-A [22], and the proposed method has a faster inference speed.

Original: Our ShuffleMixer-Tiny and ShuffleMixer reconstruct an HR image with a resolution of 1280 × 720 pixels with 0.016s and 0.021s, respectively.
Revised: Our ShuffleMixer-Tiny and ShuffleMixer reconstruct an HR image with a resolution of 1280 × 720 pixels with 0.016s and 0.021s, respectively.

Original: As a comparison, the runtimes are 0.017s, 0.019s, and 0.031s for CARN-M,
Revised: As a comparison, the runtimes are 0.017s, 0.019s, and 0.031s for CARN-M,

Original: CARN, and LAPAR-A. Note that Pytorch has poor support for large-kernel depth-wise convolution; employing optimized depth-wise convolutions can further accelerate the inference time of our method, as suggested in [7].
Revised: CARN, and LAPAR-A. Note that Pytorch has poor support for large-kernel depth-wise convolution; employing optimized depth-wise convolutions can further accelerate the inference time of our method, as suggested in [7].

Original: All these results demonstrate the effectiveness of our method.
Revised: All these results demonstrate the effectiveness of our method.

Original: Figure 3 presents visual comparisons on Set14 and Urban100 datasets for a × 4 scale.
Revised: Figure 3 presents visual comparisons on Set14 and Urban100 datasets for a × 4 scale.

Original: The qualitative comparison results demonstrate that our proposed methods can produce more visually pleasing results.
Revised: The qualitative comparison results demonstrate that our proposed methods can produce more visually pleasing results.

Original: The structures and details are better recovered.
Revised: The structures and details are better recovered.

Original: We further evaluate our approach on real low-quality images.
Revised: We further evaluate our approach on real low-quality images.

Original: One example from the historical dataset [21] is shown in Figure 4.
Revised: One example from the historical dataset [21] is shown in Figure 4.

Original: The results by [15, 22] show visible artifacts.
Revised: The results by [15, 22] show visible artifacts.

Original: Our method and
Revised: Our method and

Original: CARN [1] generate smooth details, but our results have a clearer structure.
Revised: CARN [1] generate smooth details, but our results have a clearer structure.

Original: 4.3 Analysis and Discussions
Revised: 4.3 Analysis and discussions

Original: The core idea of ShuffleMixer lies in the shuffle mixer layer, feature mixing block, and large kernel convolution.
Revised: The core idea of ShuffleMixer lies in the shuffle mixer layer, feature mixing block, and large kernel convolution.

Original: In this subsection, we evaluate them on the proposed tiny model and train them on the × 4 DIV2K dataset [37].
Revised: In this subsection, we evaluate them on the proposed tiny model and train them on the × 4 DIV2K dataset [37].

Original: Effectiveness of the shuffle mixer layer.
Revised: Effectiveness of the shuffle mixer layer.

Original: To verify the efficiency of the shuffle mixer, we use
Revised: To verify the efficiency of the shuffle mixer, we use

Original: ConvMixer [39] blocks to build a baseline model.
Revised: ConvMixer [39] blocks to build a baseline model.

Original: Unlike the original ConvMixer module, we replace
Revised: Unlike the original ConvMixer module, we replace

Original: BatchNorm with LayerNorm and apply it only before the point-wise MLP layer because BatchNorm tends to bring artifacts in the generated results [25, 41].
Revised: BatchNorm with LayerNorm and apply it only before the point-wise MLP layer because BatchNorm tends to bring artifacts in the generated results [25, 41].

Original: The kernel of depth-wise convolution is set to 3, and the number of channels is 32.
Revised: The kernel of depth-wise convolution is set to 3, and the number of channels is 32.

Original: When applying the channel splitting and shuffling (CSS) strategy, the number of parameters is reduced from 55.9K to 24.7K, and the performance is also 0.13dB lower than the baseline.
Revised: When applying the channel splitting and shuffling (CSS) strategy, the number of parameters is reduced from 55.9K to 24.7K, and the performance is also 0.13dB lower than the baseline.

Original: This result reflects that the split operation limits the representation capability of the channel projection layer.
Revised: This result reflects that the split operation limits the representation capability of the channel projection layer.

Original: To compensate for the lack of PSNR, we repeat the CSS-based projection layer to enable more cross-group feature mixing (denoted by CDC).
Revised: To compensate for the lack of PSNR, we repeat the CSS-based projection layer to enable more cross-group feature mixing (denoted by CDC).

Original: Table 2(a) shows a quantitative comparison where we find that CDC achieves similar performance to the baseline model while reducing parameters from 55.9K to 35.5K and FLOPs from 5.2G to 3.8G.
Revised: Table 2(a) shows a quantitative comparison where we find that CDC achieves similar performance to the baseline model while reducing parameters from 55.9K to 35.5K and FLOPs from 5.2G to 3.8G.

Original: Effectiveness of the feature mixing block.
Revised: Effectiveness of the feature mixing block.

Original: To validate the effectiveness of the proposed feature mixing block, we take the CDC model as a baseline and embed a convolution layer with a kernel size of 3 × 3 pixels after each two shuffle mixer layers.
Revised: To validate the effectiveness of the proposed feature mixing block, we take the CDC model as a baseline and embed a convolution layer with a kernel size of 3 × 3 pixels after each two shuffle mixer layers.

Original: This conduct brings a gain of 0.13dB over
Revised: This conduct brings a gain of 0.13dB over

Original: [Figure]
Revised: [Figure]

Original: the baseline.
Revised: the baseline.

Original: We then investigate feature element-wise summation or concatenation to analyse the effect of feature fusion manners.
Revised: We then investigate feature element-wise summation or concatenation to analyse the effect of feature fusion manners.

Original: The input features are aggregated by the aforementioned ways and refined by a convolutional layer with a kernel size of 3 × 3 , which we denote as S-Conv and C-Conv, respectively.
Revised: The input features are aggregated by the aforementioned ways and refined by a convolutional layer with a kernel size of 3 × 3 , which we denote as S-Conv and C-Conv, respectively.

Original: Table 2(b) shows that they all improve over the baseline; C-Conv achieves better PSNR performance while having more computational cost.
Revised: Table 2(b) shows that they all improve over the baseline; C-Conv achieves better PSNR performance while having more computational cost.

Original: Figure 5 exhibits the average feature map of the channel axis before the upsampler module, which illustrates that enhancing local connectivity helps grab more delicate high-frequency contents.
Revised: Figure 5 exhibits the average feature map of the channel axis before the upsampler module, which illustrates that enhancing local connectivity helps grab more delicate high-frequency contents.

Original: Based on the S-Conv, we replace the convolution layer with basic residual blocks (S-ResBlock) and Fused-MBConv (S-FMBConv).
Revised: Based on the S-Conv, we replace the convolution layer with basic residual blocks (S-ResBlock) and Fused-MBConv (S-FMBConv).

Original: Table 2(b) shows that
Revised: Table 2(b) shows that

Original: S-FMBConv obtains a balanced trade-off between model complexity and SR performance.
Revised: S-FMBConv obtains a balanced trade-off between model complexity and SR performance.

Original: Thus, we choose S-FMBConv to strengthen the local interactions within features in this paper.
Revised: Thus, we choose S-FMBConv to strengthen the local interactions within features in this paper.

Original: Effectiveness of large depth-wise convolution.
Revised: Effectiveness of large depth-wise convolution.

Original: To demonstrate the effect of a large kernel, we use different kernel sizes ranging from 3 × 3 to 21 × 21 pixels and test their performance separately.
Revised: To demonstrate the effect of a large kernel, we use different kernel sizes ranging from 3 × 3 to 21 × 21 pixels and test their performance separately.

Original: Table 3 shows that using a larger kernel size will improve the performance.
Revised: Table 3 shows that using a larger kernel size will improve the performance.

Original: In particular, the PSNR value of the method using a kernel size of 7 × 7 pixels is 0.07dB higher than that of using 3 ×kernel size while only increasing 12K parameters and 0.8G FLOPs.
Revised: In particular, the PSNR value of the method using a kernel size of 7 × 7 pixels is 0.07dB higher than that of using 3 ×kernel size while only increasing 12K parameters and 0.8G FLOPs.

Original: In addition, we note that the performance gains are minor when the kernel size is larger than 7 × 7 pixels.
Revised: In addition, we note that the performance gains are minor when the kernel size is larger than 7 × 7 pixels.

Original: Thus, the kernel size is set to be 7 × 7 pixels as a trade-off between accuracy and model complexity in this paper.
Revised: Thus, the kernel size is set to be 7 × 7 pixels as a trade-off between accuracy and model complexity in this paper.

Original: Moreover, we investigate the effect of increasing the depth ( #B ) or width ( #C ) of the proposed tiny model on the reconstruction performance.
Revised: Moreover, we investigate the effect of increasing the depth ( #B ) or width ( #C ) of the proposed tiny model on the reconstruction performance.

Original: Table 4 shows that increasing the depth or width of the model with a small kernel size does not yield as good a performance gain as enlarging the kernel size.
Revised: Table 4 shows that increasing the depth or width of the model with a small kernel size does not yield as good a performance gain as enlarging the kernel size.

Original: These results also demonstrate the effectiveness of employing large depth-wise convolution.
Revised: These results also demonstrate the effectiveness of employing large depth-wise convolution.

Original:  Conclusion
Revised:  Conclusion

Original: In this paper, we propose a lightweight deep model to solve the image super-resolution problem.
Revised: In this paper, we propose a lightweight deep model to solve the image super-resolution problem.

Original: The proposed deep model, i.e., ShuffleMixer, contains a shuffler mixer layer with a larger effective receptive field to extract non-local feature representations efficiently.
Revised: The proposed deep model, i.e., ShuffleMixer, contains a shuffler mixer layer with a larger effective receptive field to extract non-local feature representations efficiently.

Original: We also introduce the FusedMBConv to modulate the local connectivity of features generated by the shuffler mixer layer, which is critical for improving SR performance.
Revised: We also introduce the FusedMBConv to modulate the local connectivity of features generated by the shuffler mixer layer, which is critical for improving SR performance.

Original: We both qualitatively and quantitatively evaluate the proposed ShuffleMixer on commonly used benchmarks.
Revised: We both qualitatively and quantitatively evaluate the proposed ShuffleMixer on commonly used benchmarks.

Original: Experimental results demonstrate that the proposed ShuffleMixer is much more efficient while achieving competitive performance than the state-of-the-art methods.
Revised: Experimental results demonstrate that the proposed ShuffleMixer is much more efficient while achieving competitive performance than the state-of-the-art methods.

Original: Acknowledgments.
Revised: Acknowledgments.

Original: This work has been supported in part by the National Key R&D Program of
Revised: This work has been supported in part by the National Key R&D Program of

Original: China (No. 2018AAA0102001), the National Natural Science Foundation of China (Nos. 61922043, 61872421, 61925204), and the Fundamental Research Funds for the Central Universities (No.
Revised: China (No. 2018AAA0102001), the National Natural Science Foundation of China (Nos. 61922043, 61872421, 61925204), and the Fundamental Research Funds for the Central Universities (No.

Original: Broader Impact
Revised: Broader Impact

Original: This paper is an exploratory work on lightweight and efficient image super-resolution using a largekernel ConvNet.
Revised: This paper is an exploratory work on lightweight and efficient image super-resolution using a largekernel ConvNet.

Original: This approach can be deployed in some resource-constrained environments to improve image quality, such as processing pictures taken by smartphones and reducing bandwidth during video calls or meetings.
Revised: This approach can be deployed in some resource-constrained environments to improve image quality, such as processing pictures taken by smartphones and reducing bandwidth during video calls or meetings.

Original: However, super-resolution technology has also brought some negative effects, such as criminals using this technology to enhance people’s facial or body features, thereby allowing identity information to leak.
Revised: However, super-resolution technology has also brought some negative effects, such as criminals using this technology to enhance people’s facial or body features, thereby allowing identity information to leak.

Original: It is worth noting that the positive social impact of image super-resolution far outweighs the potential problems.
Revised: It is worth noting that the positive social impact of image super-resolution far outweighs the potential problems.

Original: We call on people to use this technology and its derivative applications without harming the personal interests of the public
Revised: We call on people to use this technology and its derivative applications without harming the personal interests of the public

Original: Checklist
Revised: Checklist

Original: The checklist follows the references.
Revised: The checklist follows the references.

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Revised: For each question, change the default [TODO] to [Yes] , [No] or [N/A] .

Original: You are strongly encouraged to include a justification to your answer , either by referencing the appropriate section of your paper or providing a brief inline description.
Revised: You are strongly encouraged to include a justification to your answer , either by referencing the appropriate section of your paper or providing a brief inline description.

Original: Please do not modify the questions and only use the provided macros for your answers.
Revised: Please do not modify the questions and only use the provided macros for your answers.

Original: Note that the
Revised: Note that the

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Revised: Checklist section does not count towards the page limit.

Original: In your paper, please delete this instructions block and only keep the Checklist section heading above along with the questions/answers below.
Revised: In your paper, please delete this instructions block and only keep the Checklist section heading above along with the questions/answers below.

Original: For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope?
Revised: For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope?

Original: [Yes] (b) Did you describe the limitations of your work?
Revised: [Yes] (b) Did you describe the limitations of your work?

Original: [No] (c) Did you discuss any potential negative societal impacts of your work?
Revised: [No] (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes] (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?
Revised: [Yes] (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?

Original: [Yes] 2.
Revised: [Yes] 2.

Original: If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results?
Revised: If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results?

Original: [N/A] (b) Did you include complete proofs of all theoretical results?
Revised: [N/A] (b) Did you include complete proofs of all theoretical results?

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Original: If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?
Revised: If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?

Original: [Yes] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?
Revised: [Yes] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?

Original: [Yes] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?
Revised: [Yes] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?

Original: [Yes] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)?
Revised: [Yes] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)?

Original: [Yes] 4.
Revised: [Yes] 4.

Original: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators?
Revised: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators?

Original: [Yes] (b) Did you mention the license of the assets?
Revised: [Yes] (b) Did you mention the license of the assets?

Original: [Yes] (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: [Yes] (c) Did you include any new assets either in the supplemental material or as a URL?

Original: [No] (d) Did you discuss whether
Revised: [No] (d) Did you discuss whether

Original: and how consent was obtained from people whose data you’re using/curating?
Revised: and how consent was obtained from people whose data you’re using/curating?

Original: [N/A] (e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content?
Revised: [N/A] (e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content?

Original: [N/A] 5.
Revised: [N/A] 5.

Original: If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable?
Revised: If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable?

Original: [N/A]
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Original: (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable?
Revised: (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable?

Original: [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation?
Revised: [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation?

Original: [N/A]
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