scholarly journals Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

2021 ◽  
Vol 11 (15) ◽  
pp. 7006
Author(s):  
Chang-Hwan Son
Keyword(s):  
Adaptive Learning ◽  
Additive Model ◽  
Input Image ◽  
Base Layer ◽  
Residual Learning ◽  
Convolution Model ◽  
Continuous Tone ◽  
Inverse Halftoning ◽  
Image Difference ◽  
Gaussian Convolution

Layer decomposition to separate an input image into base and detail layers has been steadily used for image restoration. Existing residual networks based on an additive model require residual layers with a small output range for fast convergence and visual quality improvement. However, in inverse halftoning, homogenous dot patterns hinder a small output range from the residual layers. Therefore, a new layer decomposition network based on the Gaussian convolution model (GCM) and a structure-aware deblurring strategy is presented to achieve residual learning for both the base and detail layers. For the base layer, a new GCM-based residual subnetwork is presented. The GCM utilizes a statistical distribution, in which the image difference between a blurred continuous-tone image and a blurred halftoned image with a Gaussian filter can result in a narrow output range. Subsequently, the GCM-based residual subnetwork uses a Gaussian-filtered halftoned image as the input, and outputs the image difference as a residual, thereby generating the base layer, i.e., the Gaussian-blurred continuous-tone image. For the detail layer, a new structure-aware residual deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of the base layer, the SARDS uses the predicted base layer as the input, and outputs the deblurred version. To more effectively restore image structures such as lines and text, a new image structure map predictor is incorporated into the deblurring network to induce structure-adaptive learning. This paper provides a method to realize the residual learning of both the base and detail layers based on the GCM and SARDS. In addition, it is verified that the proposed method surpasses state-of-the-art methods based on U-Net, direct deblurring networks, and progressively residual networks.

scholarly journals Detail Enhancement for Infrared Images Based on Propagated Image Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yishu Peng ◽  
Yunhui Yan ◽  
Jiuliang Zhao
Keyword(s):  
Dynamic Range ◽  
Gain Control ◽  
Bilateral Filter ◽  
Input Image ◽  
Base Layer ◽  
Infrared Images ◽  
Image Filter ◽  
Histogram Modification ◽  
Detail Enhancement ◽  
Infrared Data

For displaying high-dynamic-range images acquired by thermal camera systems, 14-bit raw infrared data should map into 8-bit gray values. This paper presents a new method for detail enhancement of infrared images to display the image with a relatively satisfied contrast and brightness, rich detail information, and no artifacts caused by the image processing. We first adopt a propagated image filter to smooth the input image and separate the image into the base layer and the detail layer. Then, we refine the base layer by using modified histogram projection for compressing. Meanwhile, the adaptive weights derived from the layer decomposition processing are used as the strict gain control for the detail layer. The final display result is obtained by recombining the two modified layers. Experimental results on both cooled and uncooled infrared data verify that the proposed method outperforms the method based on log-power histogram modification and bilateral filter-based detail enhancement in both detail enhancement and visual effect.

scholarly journals Gradient-Guided Residual Learning for Inverse Halftoning and Image Expanding

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 50995-51007 ◽  
Author(s):  
Jin Yuan ◽  
Chao Pan ◽  
Yan Zheng ◽  
Xianyi Zhu ◽  
Zheng Qin ◽  
...  
Keyword(s):  
Residual Learning ◽  
Inverse Halftoning

scholarly journals Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

2020 ◽  
Vol 10 (20) ◽  
pp. 7233
Author(s):  
Mohd Asyraf Zulkifley ◽  
Siti Raihanah Abdani ◽  
Nuraisyah Hani Zulkifley
Keyword(s):  
Image Registration ◽  
Mean Squared Error ◽  
Bone Age ◽  
Absolute Error ◽  
Input Image ◽  
Training Dataset ◽  
Negative Consequences ◽  
X Ray ◽  
Convolution Model ◽  
Growth Disorder

One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. It is important to detect any growth disorder as early as possible, so that mitigation treatment can be administered with less negative consequences. Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner–Whitehouse that assesses selected regions in the hand X-ray images. This approach relies heavily on the medical personnel experience, which produces a high intra-observer bias. Therefore, an automated bone age prediction system with image registration using hand X-ray images is proposed in order to complement the inexperienced doctors by providing the second opinion. The system relies on an optimized regression network using a novel residual separable convolution model. The regressor network requires an input image to be 299 × 299 pixels, which will be mapped to the predicted bone age through three modules of the Xception network. Moreover, the images will be pre-processed or registered first to a standardized and normalized pose using separable convolutional neural networks. Three steps image registration are performed by segmenting the hand regions, which will be rotated using angle calculated from four keypoints of interest, before positional alignment is applied to ensure the region of interest is located in the middle. The hand segmentation is based on DeepLab V3 plus architecture, while keypoints regressor for angle alignment is based on MobileNet V1 architecture, where both of them use separable convolution as the core operators. To avoid the pitfall of underfitting, synthetic data are generated while using various rotation angles, zooming factors, and shearing images in order to augment the training dataset. The experimental results show that the proposed method returns the lowest mean absolute error and mean squared error of 8.200 months and 121.902 months2, respectively. Hence, an error of less than one year is acceptable in predicting the bone age, which can serve as a good supplement tool for providing the second expert opinion. This work does not consider gender information, which is crucial in making a better prediction, as the male and female bone structures are naturally different.

scholarly journals Infrared Image Adaptive Enhancement Guided by Energy of Gradient Transformation and Multiscale Image Fusion

2020 ◽  
Vol 10 (18) ◽  
pp. 6262
Author(s):  
Feiran Chen ◽  
Jianlin Zhang ◽  
Jingju Cai ◽  
Tao Xu ◽  
Gang Lu ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Infrared Image ◽  
High Dynamic Range ◽  
Input Image ◽  
Base Layer ◽  
Dynamic Range Compression ◽  
Novel Approach ◽  
Adaptive Enhancement ◽  
High Dynamic ◽  
Detail Enhancement

The detail enhancement and dynamic range compression of infrared (IR) images is an important issue and a necessary practical application in the domain of IR image processing. This paper provides a novel approach to displaying high dynamic range infrared images on common display equipment with appropriate contrast and clear detail information. The steps are chiefly as follows. First, in order to protect the weak global details in different regions of the image, we adjust the original normalized image into multiple brightness levels by adaptive Gamma transformation. Second, each brightness image is decomposed into a base layer and several detail layers by the multiscale guided filter. Details in each image are enhanced separately. Third, to obtain the image with global details of the input image, enhanced images in each brightness are fused together. Last, we filter out the outliers and adjust the dynamic range before outputting the image. Compared with other conventional or cutting-edge methods, the experimental results demonstrate that the proposed approach is effective and robust in dynamic range compression and detail information enhancement of IR image.

scholarly journals Color image encryption via compressive sensing and chaotic systems

2020 ◽  
Vol 309 ◽  
pp. 03017 ◽  
Author(s):  
Kaige Zhu ◽  
Jinli Cheng
Keyword(s):  
Compressive Sensing ◽  
Image Encryption ◽  
Adaptive Learning ◽  
Color Image ◽  
Input Image ◽  
Diffusion Method ◽  
Color Image Encryption ◽  
Plain Image ◽  
Low Dimensional ◽  
Image Encryption Algorithm

In this paper, we design a color image encryption algorithm based on chaotic system and block compressive sensing. Firstly, the sparse representation of the plain-image is obtained by an adaptive learning dictionary. Secondly, the key streams are produced from two excellent low-dimensional chaotic maps, where updating the initial values and parameters rely on the SHA-384 and the input image. Thirdly, three measurement matrices of R, G, B components are constructed from the chaotic sequences, respectively. Finally, a random rows and columns diffusion method is performed on the encrypted image. Experimental results and safety analysis prove that the proposed scheme has excellent performance.

scholarly journals Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review

2020 ◽  
Vol 10 (4) ◽  
pp. 1521
Author(s):  
Mei Li ◽  
Erhu Zhang ◽  
Yutong Wang ◽  
Jinghong Duan ◽  
Cuining Jing
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Objective Evaluation ◽  
Future Research ◽  
Multi Stage ◽  
Continuous Tone ◽  
Inverse Halftoning ◽  
Visual Artifacts ◽  
Detail Loss ◽  
Halftone Images

Inverse halftoning is an ill-posed problem that refers to the problem of restoring continuous-tone images from their halftone versions. Although much progress has been achieved over the last decades, the restored images still suffer from detail loss and visual artifacts. Recent studies show that inverse halftoning methods based on deep learning are superior to other traditional methods, and thus this paper aimed to systematically review the inverse halftone methods based on deep learning, so as to provide a reference for the development of inverse halftoning. In this paper, we firstly proposed a classification method for inverse halftoning methods on the basis of the source of halftone images. Then, two types of inverse halftoning methods for digital halftone images and scanned halftone images were investigated in terms of network architecture, loss functions, and training strategies. Furthermore, we studied existing image quality evaluation including subjective and objective evaluation by experiments. The evaluation results demonstrated that methods based on multiple subnetworks and methods based on multi-stage strategies are superior to other methods. In addition, the perceptual loss and the gradient loss are helpful for improving the quality of restored images. Finally, we gave the future research directions by analyzing the shortcomings of existing inverse halftoning methods.

scholarly journals Inverse Halftoning Based on Weighted Nuclear Norm Minimization

2021 ◽  
Author(s):  
Jun Yang ◽  
Xiaojun Jia ◽  
Zihao Liu ◽  
Li Chen ◽  
Ying Wu
Keyword(s):  
Nuclear Norm ◽  
Nuclear Norm Minimization ◽  
Halftone Image ◽  
Norm Minimization ◽  
The Arts ◽  
Continuous Tone ◽  
Inverse Halftoning ◽  
Ill Posed ◽  
Nonlocal Regularization ◽  
Weighted Nuclear Norm Minimization

Abstract The inverse halftoning technology refers to restore a continuous-tone image from a halftone image with only bi-level pixes. However, recovering the continuous images from their halftoned ones is normally ill-posed, which making the inverse halftoning algorithm very challenging. In this paper, we propose an optimization model with two alternate projections (TAP) for image inverse halftoning under the weighted nuclear norm minimization (WNNM) framework. The main contributions are two-folds. First, the WNNM nonlocal regularization term is established, which offers a powerful mechanism of nonlocal self-similarity to ensure a more reliable estimation. Second, the alternate minimization projections are formulated for solving the image inverse halftoning, which reconstructs the continuous-tone image without destroying the image details and structures. The experiment results shown that the proposed method outperformed the state of the arts in terms of both objective measurements and subjective visual performance.

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