Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as ll as to any other state-of-the-art methods.  re able to train networks with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and symmetric ight initialization.  provide further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives, generalization with respect to training dataset size, and generalization with respect to JPEG quality level.
['Pavel Svoboda', 'Michal Hradis', 'David Barina', 'Pavel Zemcik']
I am writing a research paper on a idea Compression Artifacts Removal Using Convolutional Neural Networks
[{"from": "human", "value": "I am writing a research paper on a idea Compression Artifacts Removal Using Convolutional Neural Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as ll as to any other state-of-the-art methods.  re able to train networks with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and symmetric ight initialization.  provide further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives, generalization with respect to training dataset size, and generalization with respect to JPEG quality level."}]