An Effective Mixed-Precision Quantization Method for Joint Image Deblurring and Edge Detection

Abstract

Deploying deep neural networks (DNNs) for joint image deblurring and edge detection often faces challenges due to large model size, which restricts practical applicability. Although quantization has emerged as an effective solution to this issue, conventional quantization methods frequently struggle to optimize for the unique characteristics of the targeted model. This paper introduces a mixed-precision quantization method that dynamically adjusts quantization precision based on the edge regions of the input image. High-precision quantization is applied to edge neighborhoods to preserve critical details, while low-precision quantization is employed in other areas to reduce computational overhead. In addition, a zero-skipping computation strategy is designed for model deployment, thereby enhancing computational efficiency when processing sparse input feature maps. The experimental results demonstrate that the proposed method significantly outperforms existing quantization methods in model accuracy across different edge neighborhood settings (achieving 97.54% to 98.23%) while also attaining optimal computational efficiency under both 3 × 3 and 5 × 5 edge neighborhood configurations.