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

arXiv cs.AI / 4/25/2026

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Key Points

Mobile image enhancement models can lose output quality when they are quantized for real on-device use, creating a training–deployment mismatch.
The proposed approach targets mobile deployment with a hierarchical network that uses gated encoder blocks and multi-scale refinement to retain fine visual details.
It adds Quantization-Aware Training (QAT) so the model learns under low-precision constraints, reducing the quality drop typically seen with post-training quantization (PTQ).
Experiments show the method achieves high-fidelity enhancement while keeping computational overhead low enough for standard mobile devices.
The accompanying code will be released at https://github.com/GenAI4E/QATIE.git.

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.