Image restoration is a classic but challenging problem in the computer vision field.
In recent, deep learning-based methods have achieved superior performance by the huge capacity of neural networks and the large volume of the dataset.
However, are these heavy models applicable in real-world applications?
If not suitable, what are the directions the deep methods would take?
To answer these questions, the thesis explore three aspects: model efficiency, data efficiency, and multi-modal distortion.
In model efficiency, We define the "efficiency" as both network size and the computation cost to run the network.
Many studies have focused on the former alone, but in reality, the latter one is the key ingredient because of the runtime latency and the battery consumption issues.
To tackle this, We devise the network structure and rethinking the training strategy to maintain the performance as much as possible while effectively advance both efficiency aspects: network size, and the number of the operations.
For data efficiency, We investigate the data augmentation and the unsupervised training in the image restoration task.
The data augmentation method is fruitful when the training dataset is small or the network capacity is large without any computation cost in runtime.
The unsupervised training assumes the scenario where only low-quality images exist, much challenging compared to the supervised regime.
These two concepts have been well analyzed in the high-level vision field, but not many in the image restoration community.
With both training strategies, We achieve the huge performance leap to the recent image restoration methods in many real-world scenarios and datasets.
Last but not least, We tackle the multi-modal distortion, in particular, when multiple distortions corrupt the different regions of image.
The single distortion restoration network or the distortion recognition-restoration pipeline system are not satisfactory in terms of both the performance and the efficiency when serving a model.
In contrast, the proposed multi-expert network based on the multi-task learning and the analysis of the multi-modal distribution performs superior restoration accuracy with reasonable computation cost and good efficiency in model serving perspective.
