Deep Burst Denoising

Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will result in blurred images. Another way of gathering more light is to capture multiple short (thus noisy) frames in a "burst" and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution

A Comparison of Image Denoising Methods

The advancement of imaging devices and countless images generated everyday pose an increasingly high demand on image denoising, which still remains a challenging task in terms of both effectiveness and efficiency. To improve denoising quality, numerous denoising techniques and approaches have been proposed in the past decades, including different transforms, regularization terms, algebraic representations and especially advanced deep neural network (DNN) architectures. Despite their sophistication, many methods may fail to achieve desirable results for simultaneous noise removal and fine detail preservation. In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications. We also introduce a new dataset for benchmarking, and the evaluations are performed from four different perspectives including quantitative metrics, visual effects, human ratings and computational cost. Our experiments demonstrate: (i) the effectiveness and efficiency of representative traditional denoisers for various denoising tasks, (ii) a simple matrix-based algorithm may be able to produce similar results compared with its tensor counterparts, and (iii) the notable achievements of DNN models, which exhibit impressive generalization ability and show state-of-the-art performance on various datasets. In spite of the progress in recent years, we discuss shortcomings and possible extensions of existing techniques. Datasets, code and results are made publicly available and will be continuously updated at https://github.com/ZhaomingKong/Denoising-Comparison.Comment: In this paper, we intend to collect and compare various denoising methods to investigate their effectiveness, efficiency, applicability and generalization ability with both synthetic and real-world experiment

Denoising Low-Dose CT Images using Multi-frame techniques

This study examines potential methods of achieving a reduction in X-ray radiation dose of Computer Tomography (CT) using multi-frame low-dose CT images. Even though a single-frame low-dose CT image is not very diagnostically useful due to excessive noise, we have found that by using multi-frame low-dose CT images we can denoise these low-dose CT images quite significantly at lower radiation dose. We have proposed two approaches leveraging these multi-frame low-dose CT denoising techniques. In our first method, we proposed a blind source separation (BSS) based CT image method using a multiframe low-dose image sequence. By using BSS technique, we estimated the independent image component and noise components from the image sequences. The extracted image component then is further donoised using a nonlocal groupwise denoiser named BM3D that used the mean standard deviation of the noise components. We have also proposed an extension of this method using a window splitting technique. In our second method, we leveraged the power of deep learning to introduce a collaborative technique to train multiple Noise2Noise generators simultaneously and learn the image representation from LDCT images. We presented three models using this Collaborative Network (CN) principle employing two generators (CN2G), three generators (CN3G), and hybrid three generators (HCN3G) consisting of BSS denoiser with one of the CN generators. The CN3G model showed better performance than the CN2G model in terms of denoised image quality at the expense of an additional LDCT image. The HCN3G model took the advantages of both these models by managing to train three collaborative generators using only two LDCT images by leveraging our first proposed method using blind source separation (BSS) and block matching 3-D (BM3D) filter. By using these multi-frame techniques, we can reduce the radiation dosage quite significantly without losing significant image details, especially for low-contrast areas. Amongst our all methods, the HCN3G model performs the best in terms of PSNR, SSIM, and material noise characteristics, while CN2G and CN3G perform better in terms of contrast difference. In HCN3G model, we have combined two of our methods in a single technique. In addition, we have introduced Collaborative Network (CN) and collaborative loss terms in the L2 losses calculation in our second method which is a significant contribution of this research study