There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Moire pattern is a common image quality degradation caused by frequency aliasing between monitors and cameras when taking screen-shot photos. The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moire pattern make its removal a challenging problem. In this paper, we propose a Moire pattern Removal Neural Network (MopNet) to solve this problem. All core components of MopNet are specially designed for unique properties of moire patterns, including the multi-scale feature aggregation addressing complex frequency, the channel-wise target edge predictor to exploit imbalanced magnitude among colour channels, and the attribute-aware classifier to characterize the diverse appearance for better modelling Moire patterns. Quantitative and qualitative experimental comparison validate the state-of-the-art performance of MopNet.

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Mop Moiré Patterns Using MopNet

Block-matching and 3D filtering algorithm (BM3D) is the current state-of-the-art for image denoising. This algorithm has a high capacity to achieve better noise removal results as compared with other existing algorithms. Nevertheless, there is still much room for improvement in this algorithm to achieve more attractive results. To address the shortcomings of BM3D filtering, our paper algorithm makes the following contributions: Firstly, the traditional hard-thresholding of the BM3D method is substituted by an adaptive filtering technique. This technique has a high capacity to acclimate and change according to the noise intensity. More accurately, in the proposed algorithm, soft-thresholding is applied to the high-noise areas, whereas the total variation filter is applied to the light-noise areas. The self-adaptation and stability of the proposed adaptive filtering technique have enabled this technique to achieve optimal noise reduction performance and preserve the high spatial frequency detail (e.g. sharp edges). Secondly, since too small threshold leaves the most amount of the noise without removing, in contrast, a too large threshold fails to maintain the significant information of the image such as edges. Accordingly, in our proposed algorithm, applying the adaptive filtering function in the first stage is based on an adaptive threshold. This threshold is adaptable and changeable according to the amount of the noise. Thirdly, an Adaptive Weight Function (AWF) that depends on the spatial distance between the reference patch and its candidate patches, is adopted in the proposed dissimilarity measurement. When the distance between the reference patch and the candidate patch is small enough (nearby patches), AWF adopts the proposed dissimilarity measurement in computing this distance. On the other hand, when the distance between the reference patch and the candidate patch is large enough (where the candidate patches are located out of the region of the reference patch), AWF adopts the k-means clustering and the Formula (21) in computing this distance. The k-means clustering is adopted at the last estimate. Utilizing the k-means clustering to partition the image into several regions and identify the boundaries between these regions obliges the block matching to search within the region of the reference patch, which leads to reducing the risk of finding poor matching. Our proposed filter is tested on various digital images for different filtering quality measures. This filter shows significant improvements over BM3D filtering in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR) index, and Structural Similarity (SSIM) index.

BM3D image denoising algorithm based on an adaptive filtering

Taking photos of optoelectronic displays is a direct and spontaneous way of transferring data and keeping records, which is widely practiced. However, due to the analog signal interference between the pixel grids of the display screen and camera sensor array, objectionable moire (alias) patterns appear in captured screen images. As the moire patterns are structured and highly variant, they are difficult to be completely removed without affecting the underneath latent image. In this paper, we propose an approach of deep convolutional neural network for demoireing screen photos. The proposed DCNN consists of a coarse-scale network and a fine-scale network. In the coarse-scale network, the input image is first downsampled and then processed by stacked residual blocks to remove the moire artifacts. After that, the fine-scale network upsamples the demoired low-resolution image back to the original resolution. Extensive experimental results have demonstrated that the proposed technique can efficiently remove the moire patterns for camera acquired screen images; the new technique outperforms the existing ones.

Demoiréing of Camera-Captured Screen Images Using Deep Convolutional Neural Network

The prevalence of digital sensors, such as digital cameras and mobile phones, simplifies the acquisition of photos. Digital sensors, however, suffer from producing Moire when photographing objects having complex textures, which deteriorates the quality of photos. Moire spreads across various frequency bands of images and is a dynamic texture with varying colors and shapes, which pose two main challenges in demoireing—an important task in image restoration. In this paper, towards addressing the first challenge, we design a multi-scale network to process images at different spatial resolutions, obtaining features in different frequency bands, and thus our method can jointly remove moire in different frequency bands. Towards solving the second challenge, we propose a dynamic feature encoding module (DFE), embedded in each scale, for dynamic texture. Moire pattern can be eliminated more effectively via DFE.Our proposed method, termed Multi-scale convolutional network with Dynamic feature encoding for image DeMoireing (MDDM), can outperform the state of the arts in fidelity as well as perceptual on benchmarks.

Multi-Scale Dynamic Feature Encoding Network for Image Demoiréing

Moire patterns on screen-shot images are mainly due to the aliasing of the grid of the display and the camera sensor, which heavily degenerated the image quality. This paper proposes an demoireing method for screen-shot images via layer decomposition on polyphase components (LDPC). The layer decomposition model separates the image into a background layer and a moire layer, which are both regularized by a patch-based Gaussian Mixture Model (GMM) prior. To enhance the distinguishability between the image patches and moire patches, the input image is first subsampled into four polyphase components, each of which is decomposed with the GMM-based layer decomposition model. The proposed model is applied on luminance (Y) channel to weaken the intensity of moire patterns, and on red, green, blue channels respectively to further remove moire patterns. Experimental results demonstrate that the proposed method is able to efficiently remove moire artifacts for screen-shot images and outperform several other methods.

Demoiréing for screen-shot images with multi-channel layer decomposition

The block-matching collaborative filtering (BM3D) denoiser has been considered as a strong performer in image denoising, but it has high computational cost in block-matching and 3D transforms, which limits its practical applications, particularly in embedded video processing systems. In this paper, we propose an integer BM3D (IBM3D) that involves only integer operations. To integerize 3D transforms, the balance of approximation accuracy and denoising performance is carefully investigated for a wide range of noise levels. We propose an integer Wiener filter and investigate its performance over the original empirical Wiener filter with both analytical analysis and experimental verifications. The Kaiser window weighting is also integerized. The experiment results show that the proposed IBM3D provides comparable denoising performance to the original BM3D, and generates even better results for high noise levels. The proposed IBM3D requires less computation than the original BM3D, and can be deployed into embedded systems without or with limited floating-point computation resources, and ported to chips with smaller circuit areas and less power consumption.

IBM3D: Integer BM3D for Efficient Image Denoising

The invention belongs to the field of computer vision, and provides a high-quality depth contour estimation method The technical scheme adopted by the invention is a binocular RGB-D camera-based depth contour estimation method; and RGB-D represents color and depth images The method comprises the steps of firstly obtaining low-resolution depth edge information; secondly obtaining a high-resolution scatter diagram of depth edge through camera calibration and image calibration operations, and performing edge interpolation to obtain a high-resolution continuous depth contour; and finally, underthe guidance and constraint of color image edge, performing correction optimization on the depth contour to generate a final depth contour image The method is mainly applied to the computer vision scenes

Binocular RGB-D camera-based depth contour estimation method

The invention belongs to the field of digital image processing. The technical purpose is to solve the problem in the prior art that a method for removing the screen photography moire does not exist, and remove the moire pattern in a screen photography image. According to the technical scheme, the method comprises the steps of providing a multiphase coverage decomposition model, and applying the model to a Y channel to achieve fading of the color strips and the high-frequency part of the moire; inputting the obtained result to an improved Laplace filter to further remove the color strips; finally, applying the multiphase coverage decomposition models respectively in RGB channels of the model for the result obtained in the last step to obtain a final result. The method is mainly applied to digital image processing occasions.

Cai Changrui

Papers

Demoiréing for screen-shot images with multi-channel layer decomposition

IBM3D: Integer BM3D for Efficient Image Denoising

Binocular RGB-D camera-based depth contour estimation method

Screen photography image moire removing method based on multichannel decomposition