  Vignetting commonly occurs as a degradation in images resulting from factors
such as lens design, improper lens hood usage, and limitations in camera
sensors. This degradation affects image details, color accuracy, and presents
challenges in computational photography. Existing vignetting removal algorithms
predominantly rely on ideal physics assumptions and hand-crafted parameters,
resulting in the ineffective removal of irregular vignetting and suboptimal
results. Moreover, the substantial lack of real-world vignetting datasets
hinders the objective and comprehensive evaluation of vignetting removal. To
address these challenges, we present Vigset, a pioneering dataset for
vignetting removal. Vigset includes 983 pairs of both vignetting and
vignetting-free high-resolution ($5340\times3697$) real-world images under
various conditions. In addition, We introduce DeVigNet, a novel frequency-aware
Transformer architecture designed for vignetting removal. Through the Laplacian
Pyramid decomposition, we propose the Dual Aggregated Fusion Transformer to
handle global features and remove vignetting in the low-frequency domain.
Additionally, we propose the Adaptive Channel Expansion Module to enhance
details in the high-frequency domain. The experiments demonstrate that the
proposed model outperforms existing state-of-the-art methods. The code, models,
and dataset are available at \url{https://github.com/CXH-Research/DeVigNet}.
