In low light or short-exposure photography the image is often corrupted by noise. While longer exposure helps reduce the noise, it can produce blurry results due to the object and camera motion. The reconstruction of a noise-less image is an ill posed problem. Recent approaches for image denoising aim to predict kernels which are convolved with a set of successively taken images (burst) to obtain a clear image. We propose a deep neural network based approach called Multi-Kernel Prediction Networks (MKPN) for burst image denoising. MKPN predicts kernels of not just one size but of varying sizes and performs fusion of these different kernels resulting in one kernel per pixel. The advantages of our method are two fold: (a) the different sized kernels help in extracting different information from the image which results in better reconstruction and (b) kernel fusion assures retaining of the extracted information while maintaining computational efficiency. Experimental results reveal that MKPN outperforms state-of-the-art on our synthetic datasets with different noise levels.
翻译:在低光或短接触摄影中,图像往往被噪音腐蚀。虽然长期接触有助于减少噪音,但由于物体和摄像运动,图像可能会产生模糊的结果。重建无噪音图像是一个问题。最近对图像进行拆离的方法是为了预测内核,这些内核与一系列相继拍摄的图像(暴动)混杂在一起,以获得清晰的图像。我们提议采用以深神经网络为基础的方法,称为多孔预测网络(MKPN),以进行爆裂图像拆解。MKPN预测的内核不仅大小不同,而且大小不同,并进行这些不同内核的聚变,导致每个像素的内核形成一个内核。我们方法的优点是两个折叠:(a)不同大小的内核有助于从图像中获取不同的信息,从而导致更好的重建;(b)内核聚变能确保保留提取的信息,同时保持计算效率。实验结果显示,MKPN在合成数据组中,其状态与噪音水平不同。