ShiftLUT: Spatial Shift Enhanced Look-Up Tables for Efficient Image Restoration
Xiaolong Zeng, Yitong Yu, Shiyao Xiong, Jinhua Hao, Ming Sun, Chao Zhou, Bin Wang

TL;DR
ShiftLUT introduces a spatial shift enhanced LUT framework that significantly enlarges the receptive field and improves image restoration performance while maintaining efficiency suitable for edge devices.
Contribution
It proposes a novel framework with learnable spatial shifts, an asymmetric dual-branch architecture, and LUT compression, achieving the largest receptive field and better performance with low overhead.
Findings
3.8× larger receptive field than TinyLUT
Over 0.21 dB PSNR improvement on benchmarks
Maintains small storage and fast inference
Abstract
Look-Up Table based methods have emerged as a promising direction for efficient image restoration tasks. Recent LUT-based methods focus on improving their performance by expanding the receptive field. However, they inevitably introduce extra computational and storage overhead, which hinders their deployment in edge devices. To address this issue, we propose ShiftLUT, a novel framework that attains the largest receptive field among all LUT-based methods while maintaining high efficiency. Our key insight lies in three complementary components. First, Learnable Spatial Shift module (LSS) is introduced to expand the receptive field by applying learnable, channel-wise spatial offsets on feature maps. Second, we propose an asymmetric dual-branch architecture that allocates more computation to the information-dense branch, substantially reducing inference latency without compromising…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image Enhancement Techniques · Image and Video Quality Assessment
