Transformer Based Technique for High Resolution Image Restoration

What is it about?

Due to the difficulty of the problem, there have been many different approaches and algorithms developed to date. Defocus deblurring, single image motion deblurring, image deraining, and image denoising are some of the further branches of picture restoration. There are numerous algorithms that are appropriate in a variety of circumstances. For instance, gaussian noise like pepper and salt noise responds well to the median filter. The adaptive filter, on the other hand, performs particularly well in DSP and ANC applications. It has been discovered that methods based on deep neural networks, such as the restormer, can occasionally outperform earlier algorithms. We look for the best algorithm for image denoising, defocus deblurring, single image motion deblurring, and picture deraining in this work. Although restormer is shown to perform better than the others in the majority of circumstances, hybrid median filter is sometimes found to perform better than all of them combined

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Why is it important?

It highlights the importance of diverse algorithms in addressing various challenges in picture restoration, emphasizing the need for selecting the most effective approach for tasks such as image denoising, defocus deblurring, single image motion deblurring, and picture deraining, with deep neural network-based methods like the restormer often showing superior performance but with exceptions where hybrid approaches like the hybrid median filter excel.

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