Digital image tampering detection is critically important in the fields such as digital forensics and media content verification. However， in real-world applications， the tampered images are often post-processed in brightness and contrast， which will weaken tampering traces and degrade performance of the existing algorithms. To address this challenge， a restoration-assisted image tampering detection network ReConWave-Net was proposed. The network was consisted of two key modules： a classification-guided image restoration module was used to perform targeted restoration of images based on the categories of image disturbances， thereby reducing the impact of brightness and contrast disturbances； and a tampering localization module was used to strengthen the feature expression and localization ability of the tampered regions through multi-scale wavelet features and contrastive learning mechanism. The proposed network was evaluated on multiple datasets under various brightness and contrast disturbances. In terms of restoration quality， compared with the unrestored post-processed images， the proposed method increased the average Peak Signal-to-Noise Ratio （PSNR） in tampered regions from 10.86 dB to 31.57 dB， and improved the average Structural SIMilarity index （SSIM） from 0.40 to 0.92； in terms of detection performance， under typical disturbances， the network had the F1 score of 0.730 and an Intersection over Union （IoU） of 0.653. It can be seen that combining targeted restoration with detection can enhance the robustness of tampering localization of post-processed images significantly.