Because noise can interfere with downstream analysis, image denoising has come to occupy an important place in the image processing toolbox. The most accurate state-of-the-art denoisers typically train on a representative dataset. But gathering a training set is not always feasible, so interest has grown in blind zero-shot denoisers that train only on the image they are denoising. The most accurate blind-zero shot methods are blind-spot networks, which mask pixels and attempt to infer them from their surroundings. Other methods exist where all neurons participate in forward inference, however they are not as accurate and are susceptible to overfitting. Here we present a hybrid approach. We first introduce a semi blind-spot network where the network can see only a small percentage of inputs during gradient update. We then resolve overfitting by introducing a validation scheme where we split pixels into two groups and fill in pixel gaps using domino tilings. Our method achieves an average PSNR increase of $0.28$ and a three fold increase in speed over the current gold standard blind zero-shot denoiser Self2Self on synthetic Gaussian noise. We demonstrate the broader applicability of Pixel Domino Tiling by inserting it into a preciously published method.
翻译:由于噪音会干扰下游分析,图像脱色在图像处理工具箱中占据了重要位置。 最精确的先进牛仔队通常是在具有代表性的数据集上训练。 但是,收集一套训练器并不总是可行, 所以对盲人零光牛仔队的兴趣已经增加, 训练的盲零光牛仔队只对正在脱色的图像进行训练。 最准确的盲零射球队的方法是盲点网络, 遮盖像素, 并试图从周围推断出来。 在其他方法中, 所有神经元都参与前方推断, 但是它们并不准确, 并且容易过度适应。 这里我们展示了一个混合方法。 我们首先引入了一个半盲点网络, 网络在更新梯度时只能看到一小部分投入。 我们然后通过引入一个校准方案, 将像素分成两个组, 并用多米诺提林来填补像素的缺口。 我们的方法实现了平均PSNR增加0. 28美元, 并且比当前金标准的零射自2自圆速度增加3倍。 我们用一个更宽的合成高的声压方法展示了它在合成高音中。