Deep neural networks (DNNs) are vulnerable to adversarial attacks, which inject subtle perturbations into input data, leading to misclassifications. Detecting adversarial examples before classification can effectively mitigate the risk of misclassification. However, existing detection methods typically employ global reconstruction with a single strategy, which not only disrupts clean regions but also fails to adapt to varying image complexity, often overprocessing simple areas while underprocessing complex ones, ultimately reducing detection accuracy. To address these issues, we propose a novel detection approach using image-adaptive local reconstruction (RIDA). Instead of applying uniform global reconstruction, RIDA selectively reconstructs classification-relevant regions and dynamically adjusts the reconstruction strategy based on local complexity: smooth regions are denoised with lightweight filtering, while structurally complex areas undergo refined restoration using deep residual modeling. This targeted reconstruction enhances the difference between pre- and post-reconstruction predictions, which are concatenated and classified by a lightweight SVM to detect adversarial examples. Experimental results on CIFAR-10 and ImageNet demonstrate that RIDA significantly improves adversarial example detection accuracy over baseline methods across diverse attack types.