linlhc / pridnet-pytorch

A pytorch implementation of PRIDNet for Image Denoising. This is the Open source of the paper 「Pyramid Real Image Denoising Network」, which has shown extraordinary capability in terms of both quantitative measure and visual perception quality.

Pyramid Real Image Denoising Network

A pytorch implementation for the paper "Pyramid Real Image Denoising Network". ( VCIP 2019 oral )

Paper Link : Pyramid Real Image Denoising Network

Training dataset : SIDD Medium Dataset

Validation dataset : SIDD Validation data

Testing dataset : SIDD Benchmark, DND, NC12

While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noiseand denoising, they still perform poorly on real-world noisyimages. The main reason is that the real-world noise is moresophisticated and diverse. To tackle the issue of blind denoising,in this paper, we propose a novel pyramid real image denoisingnetwork (PRIDNet), which contains three stages. First, the noiseestimation stage uses channel attention mechanism to recalibratethe channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extractmulti-scale features. Third, the stage of feature fusion adopts akernel selecting operation to adaptively fuse multi-scale features.Experiments on two datasets of real noisy photographs demon-strate that our approach can achieve competitive performancein comparison with state-of-the-art denoisers in terms of bothquantitative measure and visual perception quality.

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Requirements

• python==3.8
• numpy==1.22.3
• opencv_python_headless==4.5.5.64
• torch==1.8.1