中圖分類(lèi)號(hào)：TP391 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.244857
中文引用格式： 侯明，梁文杰. 基于改進(jìn)AOD-Net的圖像去霧算法[J]. 電子技術(shù)應(yīng)用，2024，50(4)：60-66.
英文引用格式： Hou Ming，Liang Wenjie. Image defogging algorithm based on improved AOD-Net[J]. Application of Electronic Technique，2024，50(4)：60-66.

Image defogging algorithm based on improved AOD-Net

Abstract： To address issues such as color distortion, incomplete defogging, and computational inefficiency in image defogging, this study proposes an improved image defogging algorithm based on the enhanced AOD-Net. Initially, a residual connection is introduced into the existing convolutional module, preserving the features of the first layer in the second feature fusion layer to enhance feature extraction capabilities. Subsequently, an attention module is introduced after the third feature fusion layer to strengthen the representation of crucial features in hazy images and suppress irrelevant background interference. Finally, a novel composite loss function is employed for training. Experimental results demonstrate that the proposed algorithm achieves a 3.8 dB improvement in Peak Signal-to-Noise Ratio (PSNR) and a structural similarity (SSIM) of 93.6% on a public dataset. Compared to other defogging algorithms, this algorithm exhibits superior performance in both defogging accuracy and processing efficiency.

Key words : image defogging；AOD-Net；residual connection；attention module；composite loss function