Speckle Noise Reduction using Local Binary Pattern

AbstractA novel local binary pattern (LBP) based adaptive diffusion for speckle noise reduction is presented. The LBP operator unifies traditionally divergent statistical and structural models of region analysis. We use LBP textons to classify an image around a pixel into noisy, homogenous, corner and edge regions. According to different types of regions, a variable weight is assigned in to the diffusion equation, so that our algorithm can adaptively encourage strong diffusion in homogenous/noisy regions and less on the edge/corner regions. The diffusion preserves edges, local details while diffusing more on homogenous region. The experiments results are evaluated both in terms of objective metric and the visual quality

A fourth-order PDE denoising model with an adaptive relaxation method

In this paper, an adaptive relaxation method and a discontinuity treatment of edges are proposed to improve the digital image denoising process by using the fourth-order partial differential equation (known as the YK model) first proposed by You and Kaveh. Since the YK model would generate some speckles into the denoised image, a relaxation method is incorporated into the model to reduce the formation of isolated speckles. An additional improvement is employed to handle the discontinuity on the edges of the image. In order to stop the iteration automatically, a control of the iteration is integrated into the denoising process. Numerical results demonstrate that such modifications not only make the denoised image look more natural, but also achieve a higher value of PSNR

Properties of higher order nonlinear diffusion filtering

This paper provides a mathematical analysis of higher order variational methods and nonlinear diffusion filtering for image denoising. Besides the average grey value, it is shown that higher order diffusion filters preserve higher moments of the initial data. While a maximum-minimum principle in general does not hold for higher order filters, we derive stability in the 2-norm in the continuous and discrete setting. Considering the filters in terms of forward and backward diffusion, one can explain how not only the preservation, but also the enhancement of certain features in the given data is possible. Numerical results show the improved denoising capabilities of higher order filtering compared to the classical methods

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