To address the issues of low contrast， severe noise interference， and limited resolution in sonar images under complex marine environments， as well as the limitation of the existing algorithms that mainly limit in the pixel domain processing and thus lack effective feature extraction， a Frequency-domain driven and Diffusion-based fusion for Sonar Image Enhancement algorithm （FDSIE） was proposed， so as to enhance the image by utilizing its frequency-domain features. Specifically， the algorithm comprises three components： a Compact Feature Extraction Network （CFEN）， a Frequency-Domain Diffusion Module （FDDM）， and a Frequency Recovery Fusion Module （FRFM）. Firstly， the CFEN was designed to optimize and compress channel redundant features， effectively suppressing disturbances caused by ocean turbulence and acoustic artifacts. Then， the FDDM was incorporated， in which the diffusion generation submodule was used to train， infer， and reconstruct the images； the Selective Attention Feature Enhancement module （SAFE） was employed to maintain key information integrity while improving inference speed and reducing computational resource consumption， thereby enhancing accuracy of the generated images. Finally， the FRFM was employed to fuse the low?frequency and diagonal?direction information of the images adaptively， thereby improving representation abilities of horizontal and vertical edge details， and ultimately obtaining clearer target contours and texture details. Experimental results on public sonar dataset UATD （Underwater Acoustic Target Detection） show that the proposed algorithm achieves optimal Peak Signal-to-Noise Ratio （PSNR） and Structural Similarity Index Measure （SSIM） values of 29.93 dB and 0.898， respectively， surpassing the second-best algorithms Pixel Attention Transform Mechanism （PATM） and FlowIE （Flow-based Image Enhancement framework） by 8% and 5%， respectively. In addition， the proposed algorithm achieves the Learned Perceptual Image Patch Similarity （LPIPS） reached the lowest value of 0.103， which is reduced by 34% compared to that of the second-best algorithm FlowIE. These results demonstrate that the proposed algorithm provides superior image enhancement quality and perceptual consistency in sonar image enhancement tasks.