Image Denoising is the task of removing noise from an image, e.g. the application of Gaussian noise to an image.
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In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.
Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.
Ranked #2 on Grayscale Image Denoising on BSD200 sigma10
Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).
Ranked #1 on Color Image Denoising on BSD68 sigma5
We fully exploit the hierarchical features from all the convolutional layers.
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.
Ranked #3 on Denoising on Darmstadt Noise Dataset
By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.
Ranked #1 on Scene Segmentation on SUN-RGBD
Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising.
Ranked #1 on Grayscale Image Denoising on BSD68 sigma75
With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.