arXiv:2411.11906v1 Announce Type: new Abstract: Arbitrary scale super-resolution (ASSR) aims to super-resolve low-resolution images to high-resolution images at any scale using a single model, addressing the limitations of traditional super-resolution methods that are restricted to fixed-scale factors (e.g., $times2$, $times4$). The advent of Implicit Neural Representations (INR) has brought forth a plethora of novel methodologies for ASSR, which facilitate the reconstruction of original continuous signals by modeling a continuous representation space for coordinates and pixel values, thereby enabling arbitrary-scale super-resolution. Consequently, the primary objective of ASSR is to construct a continuous representation space derived from low-resolution inputs. However, existing methods, primarily based on CNNs and Transformers, face significant challenges such as high computational complexity and inadequate modeling of long-range dependencies, which hinder their effectiveness in real-world applications. To overcome these limitations, we propose a novel arbitrary-scale super-resolution method, called $text{S}^{3}$Mamba, to construct a scalable continuous representation space. Specifically, we propose a Scalable State Space Model (SSSM) to modulate the state transition matrix and the sampling matrix of step size during the discretization process, achieving scalable and continuous representation modeling with linear computational complexity. Additionally, we propose a novel scale-aware self-attention mechanism to further enhance the network’s ability to perceive global important features at different scales, thereby building the $text{S}^{3}$Mamba to achieve superior arbitrary-scale super-resolution. Extensive experiments on both synthetic and real-world benchmarks demonstrate that our method achieves state-of-the-art performance and superior generalization capabilities at arbitrary super-resolution scales.
The article “Arbitrary Scale Super-Resolution with Scalable State Space Model” addresses the limitations of traditional super-resolution methods by introducing a novel approach called S^3Mamba. These traditional methods are restricted to fixed-scale factors, whereas S^3Mamba enables super-resolution at any scale using a single model. The authors propose a Scalable State Space Model (SSSM) to construct a continuous representation space and overcome challenges such as high computational complexity and inadequate modeling of long-range dependencies. They also introduce a scale-aware self-attention mechanism to enhance the network’s ability to perceive global important features at different scales. The article presents extensive experiments on synthetic and real-world benchmarks, demonstrating that S^3Mamba achieves state-of-the-art performance and superior generalization capabilities at arbitrary super-resolution scales.
Introducing $text{S}^{3}$Mamba: A New Approach to Arbitrary-Scale Super-Resolution
The field of super-resolution has made significant strides in recent years, pushing the boundaries of image enhancement and helping us extract more detail from low-resolution images. Traditional methods have focused on fixed-scale factors, such as doubling or quadrupling the resolution. However, the limitations of these approaches have spurred the development of arbitrary scale super-resolution (ASSR) techniques, which aim to super-resolve images to any scale using a single model.
A key component in achieving ASSR is the construction of a continuous representation space derived from low-resolution inputs. Existing methods, primarily based on convolutional neural networks (CNNs) and transformers, have faced challenges related to computational complexity and the modeling of long-range dependencies. These limitations have hindered their effectiveness in real-world applications.
In light of these challenges, we propose a novel arbitrary-scale super-resolution method, called $text{S}^{3}$Mamba, which addresses these limitations and pushes the boundaries of ASSR capabilities.
Scalable State Space Model
Central to $text{S}^{3}$Mamba is our Scalable State Space Model (SSSM), which revolutionizes the discretization process by modulating the state transition matrix and the sampling matrix of step size. By incorporating scalable and continuous representation modeling, we achieve linear computational complexity. This allows our method to handle the increasing complexity of high-resolution images without sacrificing performance.
Scale-Aware Self-Attention Mechanism
In addition to SSSM, we introduce a novel scale-aware self-attention mechanism to enhance our network’s ability to perceive global important features at different scales. This mechanism ensures that our model can adapt to and handle diverse image scales, further improving the performance of $text{S}^{3}$Mamba.
Superior Performance and Generalization
Through extensive experiments on synthetic and real-world benchmarks, we have demonstrated that $text{S}^{3}$Mamba achieves state-of-the-art performance in arbitrary-scale super-resolution. Our method not only provides superior generalization capabilities, enabling it to handle a wide range of image scales, but also surpasses existing techniques in terms of computational efficiency.
With the development of $text{S}^{3}$Mamba, we are optimistic that arbitrary-scale super-resolution will become more accessible and effective in various applications. By overcoming the limitations of traditional methods, our approach opens new doors for extracting higher quality and more detailed information from low-resolution images at any desired scale.
The paper discussed in the abstract introduces a novel method called $text{S}^{3}$Mamba for arbitrary scale super-resolution (ASSR). Traditional super-resolution methods are limited to fixed-scale factors, such as 2x or 4x, but ASSR aims to super-resolve low-resolution images to high-resolution images at any scale using a single model.
The authors highlight the limitations of existing methods, which are primarily based on convolutional neural networks (CNNs) and Transformers. These methods face challenges such as high computational complexity and inadequate modeling of long-range dependencies, which limit their effectiveness in real-world applications.
To overcome these limitations, the authors propose $text{S}^{3}$Mamba, which utilizes a Scalable State Space Model (SSSM) to construct a scalable continuous representation space. The SSSM modulates the state transition matrix and the sampling matrix of step size during the discretization process, allowing for scalable and continuous representation modeling with linear computational complexity.
Additionally, the authors introduce a scale-aware self-attention mechanism to enhance the network’s ability to perceive global important features at different scales. This mechanism further improves the performance of $text{S}^{3}$Mamba in achieving superior arbitrary-scale super-resolution.
The paper presents extensive experiments on both synthetic and real-world benchmarks to evaluate the performance of $text{S}^{3}$Mamba. The results demonstrate that their method achieves state-of-the-art performance and superior generalization capabilities at arbitrary super-resolution scales.
Overall, this paper presents a promising approach to address the limitations of traditional super-resolution methods by introducing $text{S}^{3}$Mamba. The use of a Scalable State Space Model and a scale-aware self-attention mechanism allows for effective modeling of continuous representation space and enhanced perception of global features. The experimental results validate the effectiveness of $text{S}^{3}$Mamba in achieving superior arbitrary-scale super-resolution.
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