By restoring the high-resolution(HR) gradient maps and combining gradient loss with space loss to guide the parameter optimization, the gradient branch brings additional structural constraints. Denoising preprocessing eliminates noise by learning the noise distribution and utilizing residual-skip. Especially, on the basis of the original SISR, the gradient branch is developed, and the denoising preprocessing module is designed before the SR branch.
At the same time, the advantages of GAN are still used to generate satisfying details. It includes a denoising preprocessing module and a structure-keeping branch. In this paper, we focus on eliminating noise and geometric distortion during super-resolving noisy images. However, noise and structural distortion are detrimental to SISR. Recent researches have benefited from a generative adversarial network (GAN) that promotes the development of SISR by recovering photo-realistic images. Moreover, this method exhibited high computational efficiency, thus demonstrating its practicability.īoth noise and structure matter in single image super-resolution (SISR). Meanwhile, ablation studies were executed to verify the denoising performance of each module. Experiments demonstrated that the MRF‐Net outperformed several state‐of‐the‐art model‐based and deep‐learning methods in both blind and non‐blind image denoising tests. In a residual fusion module, all maps were aggregated to generate a residual image effectively for noise removal. Multilevel feature maps are sequentially obtained through a residual projection module, where considerable contextual and spatial information was collected from the multiscale features. Therefore, multiscale feature analysis could be performed in the proposed network by dilated convolution. The function of dilated convolution is reinterpreted here and it is viewed as a complex downsampling operation. In detail, dilated convolution layers are used to enlarge the network's receptive field and learned sufficient features in a multiscale feature extracting module. In this study, a high‐resolution‐based network called multiscale residual fusion network (MRF‐Net) is proposed, which employed the spatial and contextual information of images. The existing deep‐learning methods can be conducted using two major models: Encoder–decoder and high‐resolution, where the high‐resolution model has superior resolution ability for detail description and restoration. We lastly demonstrate that a light-weight SR network with a novel texture loss, trained specifically for JDSR, outperforms any combination of state-of-the-art deep denoising and SR networks.ĭeep‐learning methods have been developed in recent years and have achieved dramatic improvements for image denoising. The best denoising PSNR can, for instance, come at the expense of a loss in high frequencies, which is detrimental for SR methods. Our evaluation also shows that applying the best denoiser in terms of reconstruction error followed by the best SR method does not yield the best result. We show that state-of-the-art SR networks perform very poorly on noisy inputs, with a loss reaching 14dB relative to noise-free inputs. W2S allows us to benchmark the combinations of 6 denoising methods and 6 SR methods. A set is comprised of noisy LR images with different noise levels, a noise-free LR image, and a corresponding high-quality HR image. W2S is comprised of 144,000 real fluorescence microscopy images, used to form a total of 360 sets of images. We propose such a novel JDSR dataset, Wieldfield2SIM (W2S), acquired using microscopy equipment and techniques. In order to study joint denoising and super-resolution (JDSR), a dataset containing pairs of noisy LR images and the corresponding HR images is fundamental. Given a noisy low-resolution (LR) input image, it is yet unclear what the best approach would be in order to obtain a noise-free high-resolution (HR) image.
These two restoration tasks are well covered in the literature, however, only separately. Denoising and super-resolution (SR) are fundamental tasks in imaging.
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