Bridging the Training-Deployment Gap: Gated Encoding and Multi-Scale Refinement for Efficient Quantization-Aware Image Enhancement

• URL: http://arxiv.org/abs/2604.21743v1
• Date: Thu, 23 Apr 2026 14:46:12 GMT
• Title: Bridging the Training-Deployment Gap: Gated Encoding and Multi-Scale Refinement for Efficient Quantization-Aware Image Enhancement
• Abstract summary: We propose an efficient image enhancement model designed specifically for mobile deployment.<n>Our approach uses a hierarchical network architecture with encoder blocks and gated multiscale refinement to preserve fine-grained visual features.<n>We incorporate Quantization-Aware Training (QAT) to simulate the effects of low-precision representation during the training process.
• Score: 1.209990810846953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image enhancement models for mobile devices often struggle to balance high output quality with the fast processing speeds required by mobile hardware. While recent deep learning models can enhance low-quality mobile photos into high-quality images, their performance is often degraded when converted to lower-precision formats for actual use on mobile phones. To address this training-deployment mismatch, we propose an efficient image enhancement model designed specifically for mobile deployment. Our approach uses a hierarchical network architecture with gated encoder blocks and multiscale refinement to preserve fine-grained visual features. Moreover, we incorporate Quantization-Aware Training (QAT) to simulate the effects of low-precision representation during the training process. This allows the network to adapt and prevents the typical drop in quality seen with standard post-training quantization (PTQ). Experimental results demonstrate that the proposed method produces high-fidelity visual output while maintaining the low computational overhead needed for practical use on standard mobile devices. The code will be available at https://github.com/GenAI4E/QATIE.git.

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