A computational time comparison is missing. Limited comparison to state-of-the-art: Only PSNR and SSIM mesures are studied (but, it’s enough for a conference paper).Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work. Please list the main weaknesses of the paper.The method shows a good performance in terms of PSNR and SSIM when compared to some state-of-the-art methods.Novelty: method does not require multiple noisy observations and external noise distribution assumptions.Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting. Please list the main strengths of the paper you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work.The results show a good performance (PSNR, SSIM).Quantitative and qualitative experiments are presented comparing the proposed technique and some state-of-the-art methods.The propose technique consists of a sub-sampler module that generates sub-sampled noisy images from the original one and an image SR module that improves the sub-sampled noisy image resolution to that of the original one.Authors proposed a self-supervised image denoising method to train a image denoising model based on single noisy fluorescence image.Please describe the contribution of the paper.Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods.We envision that Noise2SR has the potential to improve more other kind of scientific imaging quality. Benefiting from this training strategy, Noise2SR is more efficiently self-supervised and able to restore more image details from a single noisy observation. Paired noisy images of different dimensions. Our Noise2SR denoising model is designed for training with To address this issue, this paper proposed self-supervised image denoising method Noise2SR (N2SR) to train a simple and effective image denoising model based on single noisy observation. However, the training efficiency and denoising performance of existing methods are relatively low in real scene noise removal. Recently, a few self-supervised deep learning (DL) denoising methods have been proposed. However, with the limitation of microscope hardware and characteristics of the observed samples, the fluorescence microscopy images are susceptible to noise. Xuanyu Tian, Qing Wu, Hongjiang Wei, Yuyao Zhangįluorescence microscopy is a key driver to promote discoveries of biomedical research. Paper Info Reviews Meta-Review Author Feedback Post-rebuttal Meta-Reviews Back to top
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