Optimization of Methods for Restoring Motion-Coded Blurred Images Based on Deep Residual Networks
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Abstract
Motion-coded blurred image restoration is an important research topic in computer vision and intelligent imaging systems, directly affecting the accuracy of scene perception and information extraction. To address the limitations of existing methods in complex motion blur kernel estimation, deep network optimization, and multi-channel information fusion, an optimized restoration framework based on deep residual networks is proposed. The method incorporates a multi-scale residual learning architecture, enhanced skip-connection mechanisms, and a channel attention module to improve blur feature representation, training efficiency, and color information utilization. Experimental results demonstrate that the proposed approach achieves superior restoration performance, reaching a PSNR of 32.15 dB and an SSIM of 0.949, while significantly improving pattern recognition accuracy in high-speed production scenarios. The multi-scale framework effectively enhances adaptability to spatially varying motion blur, and the channel attention mechanism improves color fidelity and visual quality. Beyond industrial inspection applications, the proposed method is applicable to image reconstruction and information recovery tasks in intelligent sensing systems, including optical-electromagnetic imaging, remote sensing observation, and antenna-assisted imaging platforms, where motion-induced degradation can reduce the reliability of feature extraction and target interpretation. The study provides an effective engineering solution for high-quality image restoration and robust visual information recovery under dynamic imaging conditions.
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