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Mar 24, 2024 · This innovative strategy enables improvements in the deblurring performance of blurry text images with the additional synthetic noise.
Jul 25, 2024 · Our approach centers around a robust Maximum Consensus Framework, wherein we optimize the quantity of interest from the noisy blurry image based ...
Technical paper AAAI 2024 • February 22, 2024 • Vancouver , Canada Robust Blind Text Image Deblurring via Maximum Consensus Framework | VIDEO
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Mar 25, 2024 · Robust Blind Text Image Deblurring via Maximum Consensus Framework. Zijian Min, Gundu Mohamed Hassan, Geun-Sik Jo. 4242-4250. PDF · Video/Poster ...
Our approach centers around a robust Maximum Consensus Framework, wherein we optimize the quantity of interest from the noisy blurry image based on the maximum ...
Robust Blind Text Image Deblurring via Maximum Consensus Framework, ➖, ojs ... Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only ...
This method uses two generative networks to model the deep priors of clean image and blur kernel. But this method fails for complex ...
This work revisits the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties, and derives new insights into ...
Efficient and Interpretable Deep Blind Image Deblurring Via ... Deep Robust Image Deblurring via Blur Distilling and Information Comparison in Latent Space.
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The NIR gradient constraint is used as a new type of image regularization. •. The algorithm can accurately restore the images with uniform and non-uniform blur.
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