The NTIRE 2024 RAIM in the Wild Challenge aimed to bridge the gap between academic research and practical image restoration by providing a real-world benchmark. The challenge involved restoring images with various degradations, including noise, blur, and low-light conditions, using both paired and unpaired data. Participants were evaluated using quantitative metrics like PSNR, SSIM, and LPIPS, as well as subjective assessments by 18 experts. The challenge attracted over 200 registrations, with 39 teams submitting results, including more than 400 submissions. Top-performing methods improved state-of-the-art restoration performance and received unanimous recognition. The challenge included three phases: model design and tuning, online feedback, and final evaluation. Teams used various approaches, including GANs, diffusion models, and hybrid architectures, to enhance image restoration quality. Notable teams included MiAlgo, Xhs-IAG, So Elegant, IIP_IR, DACLIP-IR, TongJi-IPOE, ImagePhoneix, and HIT-IIL, each proposing innovative methods to address real-world image restoration challenges. The challenge provided datasets and evaluation criteria to ensure fair and comprehensive assessment of the models. The results demonstrated significant improvements in image restoration performance, with teams achieving high scores in both quantitative and subjective evaluations. The challenge highlighted the importance of generalization and practical applicability in image restoration research.The NTIRE 2024 RAIM in the Wild Challenge aimed to bridge the gap between academic research and practical image restoration by providing a real-world benchmark. The challenge involved restoring images with various degradations, including noise, blur, and low-light conditions, using both paired and unpaired data. Participants were evaluated using quantitative metrics like PSNR, SSIM, and LPIPS, as well as subjective assessments by 18 experts. The challenge attracted over 200 registrations, with 39 teams submitting results, including more than 400 submissions. Top-performing methods improved state-of-the-art restoration performance and received unanimous recognition. The challenge included three phases: model design and tuning, online feedback, and final evaluation. Teams used various approaches, including GANs, diffusion models, and hybrid architectures, to enhance image restoration quality. Notable teams included MiAlgo, Xhs-IAG, So Elegant, IIP_IR, DACLIP-IR, TongJi-IPOE, ImagePhoneix, and HIT-IIL, each proposing innovative methods to address real-world image restoration challenges. The challenge provided datasets and evaluation criteria to ensure fair and comprehensive assessment of the models. The results demonstrated significant improvements in image restoration performance, with teams achieving high scores in both quantitative and subjective evaluations. The challenge highlighted the importance of generalization and practical applicability in image restoration research.