Non-uniform Image Research Articles

Lossless imaging based on a donut-like optical sparse aperture metalens

The optical sparse aperture approach has been shown to have significant potential in enlarging the aperture size and enhancing the imaging resolution of metalenses. However, because of frequency loss caused by the sparsity and dispersion of sparse aperture arrays, the stitch method for conventional optical sparse aperture metalenses has a non-uniform imaging resolution at different azimuth angles and cannot achieve the same resolution as the corresponding full-aperture metalens. Here, we design and demonstrate the application of a donut-like optical sparse aperture metalens with a lossless imaging property using a shear design method. This metalens has a diffraction-limited resolution of 153 lp/mm for all azimuth angles, which allows us to circumvent the non-uniform imaging resolution of the conventional optical sparse aperture metalens at different azimuth angles and enables us to achieve full resolution. Furthermore, noise resistance in practical images is achieved because of the improved modulation transfer function in the high-frequency region. Such a metalens without frequency loss has significant application potential in the fields of integrated optical systems, lightweight imaging devices, and wearable optics.

Deformable multi-scale fusion network for non-uniform single image deblurring

Deformable multi-scale fusion network for non-uniform single image deblurring

Non-uniform image reconstruction for fast photoacoustic microscopy of histology imaging.

Photoacoustic microscopic imaging utilizes the characteristic optical absorption properties of pigmented materials in tissues to enable label-free observation of fine morphological and structural features. Since DNA/RNA can strongly absorb ultraviolet light, ultraviolet photoacoustic microscopy can highlight the cell nucleus without complicated sample preparations such as staining, which is comparable to the standard pathological images. Further improvements in the imaging acquisition speed are critical to advancing the clinical translation of photoacoustic histology imaging technology. However, improving the imaging speed with additional hardware is hampered by considerable costs and complex design. In this work, considering heavy redundancy in the biological photoacoustic images that overconsume the computing power, we propose an image reconstruction framework called non-uniform image reconstruction (NFSR), which exploits an object detection network to reconstruct low-sampled photoacoustic histology images into high-resolution images. The sampling speed of photoacoustic histology imaging is significantly improved, saving 90% of the time cost. Furthermore, NFSR focuses on the reconstruction of the region of interest while maintaining high PSNR and SSIM evaluation indicators of more than 99% but reducing the overall computation by 60%.

Tip-enhanced photoluminescence of monolayer MoS2 increased and spectrally shifted by injection of electrons

Abstract Using tip-enhanced photoluminescence (TEPL), we investigate micron-size monolayer MoS2 flakes. In a sequence of studies, we apply various voltages between the Ag-coated nano-tip and the MoS2 flakes and observe an intriguing result. During the TEPL measurement, we observe that the photoluminescence spectrum is blue shifted and the overall signal intensity is increased. We attribute this behavior to plasmon-induced electron injection into MoS2. Additionally, when the tip is negatively biased with respect to the sample during the TEPL measurement, the nonuniform TEPL images of MoS2 monolayer flakes containing defects are gradually changed to be uniform that reach saturation. We verify that this saturation state in TEPL can last over half a year.

A framework for the efficient enhancement of non-uniform illumination underwater image using convolution neural network

In this paper, the non-uniform illumination enhancement problem of underwater images under the artificial light sources conditions is investigated based on Convolution Neural Network (CNN). First, we propose a trainable end-to-end enhancer called NUIENet, for enhancing the non-uniform illumination of underwater images. The proposed model consists of correction network and fusion layers. The correction network adopts the encoder–decoder structure with skip connections to enhance the features of different channels in the HSV domain, and then these enhanced features are fused by the fusion layers to obtain the desired high-quality images. Second, we built an underwater images dataset using Generative Adversarial Network (GAN) and Gaussian Function. Finally, both qualitative and quantitative experimental results show that the proposed method can produce better performance compared to other state-of-the-art enhancement methods on both real-word and synthetic underwater dataset.

Non-uniform Compressive Sensing Imaging Based on Image Saliency

Non-uniform Compressive Sensing Imaging Based on Image Saliency

Radial Basis Approximations Based BEMD for Enhancement of Non-Uniform Illumination Images

An image can be degraded due to many environmental factors like foggy or hazy weather, low light conditions, extra light conditions etc. Image captured under the poor light conditions is generally known as non-uniform illumination image. Non-uniform illumination hides some important information present in an image during the image capture Also, it degrades the visual quality of image which generates the need for enhancement of such images. Various techniques have been present in literature for the enhancement of such type of images. In this paper, a novel architecture has been proposed for enhancement of poor illumination images which uses radial basis approximations based BEMD (Bi-dimensional Empirical Mode Decomposition). The enhancement algorithm is applied on intensity and saturation components of image. Firstly, intensity component has been decomposed into various bi-dimensional intrinsic mode function and residue by using sifting algorithm. Secondly, some linear transformations techniques have been applied on various bidimensional intrinsic modes obtained and residue and further on joining the transformed modes with residue, enhanced intensity component is obtained. Saturation part of an image is then enhanced in accordance to the enhanced intensity component. Final enhanced image can be obtained by joining the hue, enhanced intensity and enhanced saturation parts of the given image. The proposed algorithm will not only give the visual pleasant image but maintains the naturalness of image also.

Non-Uniform Illumination Underwater Image Enhancement via Minimum Weighted Error Entropy Loss

Non-Uniform Illumination Underwater Image Enhancement via Minimum Weighted Error Entropy Loss

Active non-uniform illumination-based underwater polarization imaging method for objects with complex polarization properties.

Active polarization imaging is one of the most effective underwater optical imaging methods that can eliminate the degradation of image contrast and clarity caused by macro-molecule scattering. However, the non-uniformity of active illumination and the diversity of object polarization properties may decrease the quality of underwater imaging. This paper proposes a non-uniform illumination-based active polarization imaging method for underwater objects with complex optical properties. Firstly, illumination homogenization in the frequency domain is proposed to extract and homogenize the natural incident light from the total receiving light. Then, the weight values of the polarized and non-polarized images are computed according to each pixel's degree of linear polarization (DoLP) in the original underwater image. By this means, the two images can be fused to overcome the problem of reflected light loss generated by the complex polarization properties of underwater objects. Finally, the fusion image is normalized as the final result of the proposed underwater polarization imaging method. Both qualitative and quantitative experimental results show that the presented method can effectively eliminate the uneven brightness of the whole image and obtain the underwater fusion image with significantly improved contrast and clarity. In addition, the ablation experiment of different operation combinations shows that each component of the proposed method has noticeable enhancement effects on underwater polarization imaging. Our codes are available in Code 1.

Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation.

Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-scale database consisting of 7039 brain T1-weighted MRI data samples. We evaluated the method by using experiments focused on three distinct topics, namely, the necessity of preprocessing steps, cross-institutional and longitudinal reproducibility, and volumetric accuracy. The optimized model, MX_RW-where "MX" is a mix of RW and nonuniform intensity normalization data and "RW" is raw data with basic preprocessing-did not require time-consuming preprocessing steps, such as nonuniform intensity normalization or image registration, for brain MRI before segmentation. Cross-institutional testing revealed that MX_RW (Dice similarity coefficient: 0.809, coefficient of variation: 4.6%, and Pearson's correlation coefficient: 0.979) exhibited a performance comparable with that of FreeSurfer (Dice similarity coefficient: 0.798, coefficient of variation: 5.6%, and Pearson's correlation coefficient: 0.973). The computation time per dataset of MX_RW was generally less than 5 s (even when tested without graphics processing units), which was notably faster than FreeSurfer. Thus, for time-restricted applications, MX_RW represents a competitive alternative to FreeSurfer.

Comparison of Non-Uniform Image Quality Caused by Anode Heel Effect between Two Digital Radiographic Systems Using a Circular Step-Wedge Phantom and Mutual Information.

The purpose of this study was to compare non-uniform image quality caused by the anode heel effect between two radiographic systems using a circular step-wedge (CSW) phantom and the normalized mutual information (nMI) metric. Ten repeated radiographic images of the CSW and contrast-detail resolution (CDR) phantoms were acquired from two digital radiographic systems with 16- and 12-degree anode angles, respectively, using various kVp and mAs. To compare non-uniform image quality, the CDR phantom was physically rotated at different orientations, and the directional nMI metrics were calculated from the CSW images. The directional visible ratio (VR) metrics were calculated from the CDR images. Analysis of variance (ANOVA) was performed to understand whether the nMI metric significantly changed with kVp, mAs, and orientations with Bonferroni correction. Mann-Whitney's U test was performed to compare the metrics between the two systems. Contrary to the VR metrics, the nMI metrics significantly changed with orientations in both radiographic systems. In addition, the system with the 12-degree anode angle exhibited less uniform image quality compared to the system with the 16-degree anode angle. A CSW phantom using the directional nMI metric can be significantly helpful to compare non-uniform image quality between two digital radiographic systems.

A non-uniform illumination image enhancement method based on fusion of events and frames

The dynamic range of standard frame-based cameras is much smaller than that of natural scenes. Images captured by standard cameras in non-uniform illumination scenes may be over-exposure, under-exposure, or a combination of both, limiting the visibility of the images. Enhancing images with uneven illumination is a tough assignment. Although most of the existing non-uniform illumination image enhancement methods can preserve details well in dark areas, they cannot regain details in bright regions and may produce noise and halos. Event cameras have a higher dynamic range and are less affected by scene lighting than standard cameras. In this paper, we utilize the complementary advantages of event cameras and standard cameras to improve the visibility of unevenly illumination images, enhance the brightness of dark areas, and recover details in bright areas. First, events and frames are converted into intensity frames by corresponding preprocessing methods, which are subjected to a gamma correction operation to obtain artificial multi-exposure image sequences. Then, the weight map of each artificially exposed image is designed using several evaluation terms, namely contrast, well-exposure, and average brightness. After that, the weight terms are combined with the Laplacian pyramid algorithm to get a fused image. Finally, the final image is created by color restoration and contrast enhancement. Quantitative and qualitative experimental results demonstrate that our method enhances the details and visibility of non-uniform illumination images more effectively than several contrast algorithms.

Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model

Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift issues. In this paper, we propose a zero-shot RS image dehazing method based on a re-degradation haze imaging model, which directly restores the haze-free image from a single hazy image. Based on layer disentanglement, we design a dehazing framework consisting of three joint sub-modules to disentangle the hazy input image into three components: the atmospheric light, the transmission map, and the recovered haze-free image. We then generate a re-degraded hazy image by mixing up the hazy input image and the recovered haze-free image. By the proposed re-degradation haze imaging model, we theoretically demonstrate that the hazy input and the re-degraded hazy image follow a similar haze imaging model. This finding helps us to train the dehazing network in a zero-shot manner. The dehazing network is optimized to generate outputs that satisfy the relationship between the hazy input image and the re-degraded hazy image in the re-degradation haze imaging model. Therefore, given a hazy RS image, the dehazing network directly infers the haze-free image by minimizing a specific loss function. Using uniform hazy datasets, non-uniform hazy datasets, and real-world hazy images, we conducted comprehensive experiments to show that our method outperforms many state-of-the-art (SOTA) methods in processing uniform or slight/moderate non-uniform RS hazy images. In addition, evaluation on a high-level vision task (RS image road extraction) further demonstrates the effectiveness and promising performance of the proposed zero-shot dehazing method.

Digital image processing-based automatic detection algorithm of cross joint trace and its application in mining roadway excavation practice

This paper proposes a digital image processing-based detection algorithm for cross joint traces of coal roadway heading face. Initially, the acquired images were preprocessed, i.e., adaptive correction was conducted for non-uniform illumination images based on the 2D gamma function. The edge detection algorithm was then applied to extract the edges of the structural plane, followed by the filtration of the non-structural plane noises. Moreover, the Hough transform algorithm was applied to extract the linear edges; finally, the edges were locally connected in accordance with the angle and distance criteria. The experimental results show that this algorithm can be used to reduce the noise caused by non-uniform illumination and avoid the mutual interference of multi-scale edges, so as to effectively extract the traces of the cross joint. Furthermore, Q-system and rock mass rating (RMR ) , were applied to conduct a quantitative evaluation on the stand-up time of unsupported roof in the four test images. The Q-system quality scores are 26.7, 43.3, 3.1, and 6.7, and the RMR quality scores are 56.84, 58.73, 48.42, and 51.42, respectively. The stand-up time of unsupported roofs with a span of 4.6 m are 30, 36, 7.7 and 14 d, respectively.

Joint strong edge and multi-stream adaptive fusion network for non-uniform image deblurring

Non-uniform motion deblurring has been a challenging problem in the field of computer vision. Currently, deep learning-based deblurring methods have made promising achievements. In this paper, we propose a new joint strong edge and multi-stream adaptive fusion network to achieve non-uniform motion deblurring. The edge map and the blurred map are jointly used as network inputs and Edge Extraction Network (EEN) guides the Deblurring Network (DN) for image recovery and to complement the important edge information. The Multi-stream Adaptive Fusion Module (MAFM) adaptively fuses the edge information and features from the encoder and decoder to reduce feature redundancy to avoid image artifacts. Furthermore, the Dense Attention Feature Extraction Module (DAFEM) is designed to focus on the severely blurred regions of blurry images to obtain important recovery information. In addition, an edge loss function is added to measure the difference of edge features between the generated and clear images to further recover the edges of the deblurred images. Experiments show that our method outperforms currently public methods in terms of PSNR, SSIM and VIF, and generates images with less blur and sharper edges.

Non-uniform illumination underwater image enhancement via events and frame fusion.

Absorption and scattering by aqueous media can attenuate light and cause underwater optical imagery difficulty. Artificial light sources are usually used to aid deep-sea imaging. Due to the limited dynamic range of standard cameras, artificial light sources often cause underwater images to be underexposed or overexposed. By contrast, event cameras have a high dynamic range and high temporal resolution but cannot provide frames with rich color characteristics. In this paper, we exploit the complementarity of the two types of cameras to propose an efficient yet simple method for image enhancement of uneven underwater illumination, which can generate enhanced images containing better scene details and colors similar to standard frames. Additionally, we create a dataset recorded by the Dynamic and Active-pixel Vision Sensor that includes both event streams and frames, enabling testing of the proposed method and frame-based image enhancement methods. The experimental results conducted on our dataset with qualitative and quantitative measures demonstrate that the proposed method outperforms the compared enhancement algorithms.

Review on Infrared Imaging Technology

The application of infrared camera-related technology is a trending research topic. By reviewing the development of infrared thermal imagers, this paper introduces several main processing technologies of infrared thermal imagers, expounds the image nonuniformity correction, noise removal, and image pseudo color enhancement of infrared thermal imagers, and briefly analyzes some main algorithms used in image processing. The technologies of blind element detection and compensation, temperature measurement, target detection, and tracking of infrared thermal imager are described. By analyzing the main algorithms of infrared temperature measurement, target detection, and tracking, the advantages and disadvantages of these technologies are put forward. At the same time, the development of multi/hyperspectral infrared remote sensing technology and its application are also introduced. The analysis shows that infrared thermal imager processing technology is widely used in many fields, especially in the direction of autonomous driving, and this review helps to expand the reader’s research ideas and research methods.

Enlarging the FOV of lensless holographic retinal projection display with two-step Fresnel diffraction

The field of view (FOV) of holographic retinal projection display (RPD) is always restricted by the diffraction angle of a spatial light modulator (SLM). In this paper, we demonstrate a large FOV holographic RPD by using two-step Fresnel diffraction calculation. By adding the pupil plane as the intermediate plane and decreasing the sampling interval, the FOV can be enlarged to nearly two times of the diffraction angle limit without any physical change. Due to the added spherical wave phase, the influence of aliasing is eliminated because the adjacent orders are projected to different viewpoints. The nonuniform image intensity caused by the sinc function modulation is compensated by image pre-processing. Optical experiments verify that the proposed method can present a uniform holographic near-eye display with a large FOV and adjustable viewpoint position.

Biopsy Needle Segmentation using Deep Networks on inhomogeneous Ultrasound Images.

In minimally invasive interventional surgery, ultrasound imaging is usually used to provide real-time feedback in order to obtain the best diagnostic results or realize treatment plans, so how to accurately obtain the position of the medical biopsy needle is a problem worthy of study. 2D ultrasound simulation images containing the medical biopsy needle are generated, and our images background is from the real breast ultrasound image. Based on the deep learning network, the images containing the medical biopsy needle are used to analyze the effectiveness of different networks for needle localization for the purpose of returning needle positions in non-uniform ultrasound images. The results show that attention U-Net performed best and can accurately reflect the real position of the medical biopsy needle. The IoU and Precision can reach 90.19% and 96.25%, and the Angular Error is 0.40°. Clinical Relevance- Based on the deep network, for 2D ultrasound images containing medical biopsy needle, the localization precision can reach 96.25% and the Angular Error is 0.40°.

An Image Importance Partition-Based Compression Method for COVID-19 Computed Tomography Scan

As the coronavirus disease 2019 (COVID-19) spreads around the world, industrial automated medical diagnosis systems have been developed, which complete a large amount of medical diagnosis work through computed tomography (CT) images. In these systems, how to quickly store and transmit such a large amount of CT image information has important research significance. In this article, a more targeted COVID-19 chest CT image codec is proposed to make image data not only occupy less space but also have higher image quality. First, the bilateral lung contours are extracted to calculate the position information of the region of interest (ROI). Then, a CT image is classified into four types of nonuniform image blocks according to the characteristics of COVID-19 chest CT images and ROI position information. Next, a series of new transformations are proposed for more efficient transform coding. Finally, a flexible quantization strategy is proposed for the adaptive quantization part. In the experiments, the proposed method is superior to some of the existing methods with similar computational complexity. At the same bit rate, it significantly improves the image quality. This means that chest CT images can still be used for disease diagnosis while taking up less space. In addition, because of the low computational complexity of the proposed method, it can be more easily embedded into the CT equipment with low computational power.