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Privacy protection has become a central issue in information science, with the General Data Protection Regulation (GDPR) significantly impacting information ecosystems. Research gaps persist in understanding the causal relationship between GDPR implementation and websites' proactive changes, such as adopting privacy compliance technologies. This study aims to examine the influence of GDPR implementation on websites' information ecosystems and identify boundary conditions that may affect this relationship. Utilizing domain‐level data from a professional platform tracking website technologies before and after GDPR implementation, which encompasses a comprehensive longitudinal dataset of over 1.2 million websites, our results indicate that GDPR implementation has increased the decision, breadth, depth, and intensity of adopting related technologies. The variance in these adoptions is significantly shaped by the differing levels of institutional impacts experienced by trailblazing versus laggard industries. Our study contributes to the literature by revealing the importance of policy factors in fostering a healthier information ecosystem. We demonstrate the complex nature of the relationship between policy factors and technology adoption, highlighting the need for researchers to consider institutional context and industry‐specific characteristics. We further discuss valuable insights for relevant stakeholders. [ABSTRACT FROM AUTHOR]

Image restoration aims to recover high‐quality clean images from degraded low‐quality ones. Deep learning‐based approaches have been a focal point in the field of image restoration. However, most methods focus solely on a single type of degradation and may not extend well to real‐world scenarios with unknown degradation. For this purpose, the present study introduces an all‐in‐one image restoration approach by designing a multi‐scale feature fusion UNet structure (MdfUNet). In summary, the proposed method exhibits two significant advantages. For starters, it implicitly fuses degradation information across multiple scales, enabling the network to extract rich hierarchical features and enhancing its generalization ability towards unknown degradations. Secondly, MdfUnet possesses strong image reconstruction capabilities. It utilizes a simple non‐linear feature optimizer to enhance skip connections, providing rich feature representations for the image reconstruction process, and ultimately generating high‐quality restored images. Extensive experimental results show the proposed method outperforms multiple baselines on deraining, dehazing, and denoising datasets. [ABSTRACT FROM AUTHOR]