Atmospheric fog and aerosols can significantly reduce visibility and distort colors in images, bringing great difficulties to advanced image recognition. Existing image dehazing algorithms often face problems such as excessive enhancement, loss of details, and insufficient dehazing. To avoid excessive enhancement and insufficient dehazing, this study proposes an image dehazing algorithm based on frequency and attention mechanisms. The algorithm adopts an encoder-decoder structure and constructs a dual-branch frequency extraction module to obtain both global and local high and low-frequency information. A frequency fusion module is then constructed to adjust the weight proportions of the high and low-frequency information. To optimize the dehazing effect, the algorithm introduces an additional channel-pixel module and a channel-pixel attention module during down sampling. Experimental results show that FANet achieves a PSNR of 40.07 dB and an SSIM of 0.9958 on the SOTS-indoor dataset, and a PSNR of 39.77 dB and an SSIM of 0.9958 on the SOTS-outdoor dataset. The proposed algorithm also achieves good results on the HSTS and Haze4k test sets. It effectively alleviates color distortion and incomplete dehazing compared with other dehazing algorithms.