DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution
M. Akin Yilmaz, Ahmet Bilican, A. Murat Tekalp

TL;DR
DiMoSR introduces a novel lightweight super-resolution architecture that uses multi-branch dilated convolutions for enhanced feature modulation, outperforming existing methods in quality and efficiency.
Contribution
The paper proposes DiMoSR, a new architecture combining feature modulation with multi-branch dilated convolutions, advancing lightweight image super-resolution techniques.
Findings
Outperforms state-of-the-art lightweight SISR methods in PSNR and SSIM.
Achieves superior results with comparable or lower computational complexity.
Provides insights into the synergy between attention mechanisms and feature modulation.
Abstract
Balancing reconstruction quality versus model efficiency remains a critical challenge in lightweight single image super-resolution (SISR). Despite the prevalence of attention mechanisms in recent state-of-the-art SISR approaches that primarily emphasize or suppress feature maps, alternative architectural paradigms warrant further exploration. This paper introduces DiMoSR (Dilated Modulation Super-Resolution), a novel architecture that enhances feature representation through modulation to complement attention in lightweight SISR networks. The proposed approach leverages multi-branch dilated convolutions to capture rich contextual information over a wider receptive field while maintaining computational efficiency. Experimental results demonstrate that DiMoSR outperforms state-of-the-art lightweight methods across diverse benchmark datasets, achieving superior PSNR and SSIM metrics with…
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Taxonomy
TopicsOptical Systems and Laser Technology · Advanced Image Processing Techniques · Image Processing Techniques and Applications
MethodsSoftmax · Attention Is All You Need
