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Found 3 papers, 2 papers with code
Domain Adaptation for Image Dehazing
By training image translation and dehazing network in an end-to-end manner, we can obtain better effects of both image translation and dehazing.
Ranked #5 on
Image Dehazing
on RESIDE-6K
GTNet: Generative Transfer Network for Zero-Shot Object Detection
FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively.
Learning a Discriminative Prior for Blind Image Deblurring
We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred images. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input image is clear or not. Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN. Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient decent algorithm to solve the proposed model. Furthermore, the proposed model can be easily extended to non-uniform deblurring. Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domain-specific image deblurring approaches.
