<p>With the widespread adoption of ultra-high-resolution (UHR) images in fields such as photography and social media, traditional image retouching methods face challenges when handling UHR portrait images, including low efficiency, poor consistency in retouching, and limited detail restoration capabilities. To systematically address all of these issues, this paper proposes an adaptive UHR portrait retouching method based on regional filters. This approach mimics the strategy employed by professional human retouchers, who perform localized adjustments based on distinct regional features. Specifically, we introduce a Feature Pyramid Network structure to fuse multi-scale features and integrate a Spatial Prior Fit Module (SPFM) module at the neck stage to enhance both spatial modeling and channel representation. Furthermore, we design a Convolutional Attention Feedforward Block (CFBK) that captures spatial and channel contextual information through local convolutional attention and a lightweight feedforward network. In addition, a Selective Enhancement Prediction Module (SEPM) is developed to selectively enhance regional features and adaptively predict filter parameters such as contrast, saturation, and brightness, enabling efficient and fine-grained portrait retouching. Extensive experiments conducted on the MIT-Adobe FiveK, PPR10K, and our proprietary ALSPP5K datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches in terms of objective metrics. For instance, on PPR10K-a, it achieves improvements of up to 5.75 dB in PSNR and 0.021 in SSIM. These results verify that our method not only maintains high-quality retouching effects but also significantly improves the aesthetic quality and regional detail consistency of images, thus confirming the effectiveness of the regional filter adaptation mechanism in portrait retouching tasks.</p>