AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation

In the image acquisition process, various forms of degradation, including noise, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from degraded versions, numerous specialized restoration methods have been developed, each targeting a specific type of degradation. Recently, all-in-one algorithms have garnered significant attention by addressing different types of degradations within a single model without requiring prior information of the input degradation type. However, these methods purely operate in the spatial domain and do not delve into the distinct frequency variations inherent to different degradation types. To address this gap, we propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task. Specifically, we first mine low- and high-frequency information from the input features, guided by the adaptively decoupled spectra of the degraded image. The extracted features are then modulated by a bidirectional operator to facilitate interactions between different frequency components. Finally, the modulated features are merged into the original input for a progressively guided restoration. With this approach, the model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on different image restoration tasks, including denoising, dehazing, deraining, motion deblurring, and low-light image enhancement. Our code is available at https://github.com/c-yn/AdaIR.

A Survey on Autonomous Driving Datasets: Data Statistic, Annotation, and Outlook

Autonomous driving has rapidly developed and shown promising performance with recent advances in hardware and deep learning methods. High-quality datasets are fundamental for developing reliable autonomous driving algorithms. Previous dataset surveys tried to review the datasets but either focused on a limited number or lacked detailed investigation of the characters of datasets. To this end, we present an exhaustive study of over 200 autonomous driving datasets from multiple perspectives, including sensor modalities, data size, tasks, and contextual conditions. We introduce a novel metric to evaluate the impact of each dataset, which can also be a guide for establishing new datasets. We further analyze the annotation process and quality of datasets. Additionally, we conduct an in-depth analysis of the data distribution of several vital datasets. Finally, we discuss the development trend of the future autonomous driving datasets.