Such a property induces considerable advantages for plug-and-play image restoration (e.g., integrating the flexibility of model-based method and effectiveness of learning-based methods) when the denoiser is discriminatively learned via deep convolutional neural network (CNN) with large modeling capacity. Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. The code and the pre-trained models are released at /megvii-research/NAFNet. 33.69 dB PSNR on GoPro (for image deblurring), exceeding the previous SOTA 0.38 dB with only 8.4% of its computational costs 40.30 dB PSNR on SIDD (for image denoising), exceeding the previous SOTA 0.28 dB with less than half of its computational costs. SOTA results are achieved on various challenging benchmarks, e.g. Thus, we derive a Nonlinear Activation Free Network, namely NAFNet, from the baseline. are not necessary: they could be replaced by multiplication or removed. To further simplify the baseline, we reveal that the nonlinear activation functions, e.g. In this paper, we propose a simple baseline that exceeds the SOTA methods and is computationally efficient. Īlthough there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods. More details of this challenge and the link to the dataset can be found in. A detailed description of all models developed in this challenge is provided in this paper. The final results are evaluated using objective metrics including PSNR, SSIM, LPIPS and KLD. All the data were captured using a RGBW sensor in both outdoor and indoor conditions. In addition, for each scene, RGBW of different noise level were provided at 0 dB, 24 dB and 42 dB. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pair. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full-resolution is introduced. To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms.
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