Image Quality Evaluation Algorithm Research Articles

An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these pipelines is the image quality evaluation and masking algorithm, which evaluate image qualities based on various factors such as cloud coverage, sky brightness, scattering light from the optical system, point-spread-function size and shape, and read-out noise. Occasionally, the algorithm requires masking of areas severely affected by noise. However, the algorithm often necessitates significant human interventions, reducing data processing efficiency. In this study, we present a deep-learning-based image quality evaluation algorithm that uses an autoencoder to learn features of high quality astronomical images. The trained autoencoder enables automatic evaluation of image quality and masking of noise affected areas. We have evaluated the performance of our algorithm using two test cases: images with point spread functions of varying full width half magnitude, and images with complex backgrounds. In the first scenario, our algorithm could effectively identify variations of the point spread functions, which can provide valuable reference information for photometry. In the second scenario, our method could successfully mask regions affected by complex regions, which could significantly increase the photometry accuracy. Our algorithm can be employed to automatically evaluate image quality obtained by different sky surveying projects, further increasing the speed and robustness of data processing pipelines.

Information Visualization Based on Visual Transmission and Multimedia Data Fusion

With the rapid development of information technology, the application media of visual identity design has been greatly broadened, and the requirements of dynamic variability and interactivity of vision have become higher and higher. The introduction of data as an element has brought new semantic endowments to visual design. To solve the problems of noisy image reconstruction and many outliers in traditional art design, this paper uses an improved phase correlation algorithm to reconstruct multimedia video images. In addition, based on the visual characteristics of human eyes, a multi-feature fusion viewpoint image quality evaluation algorithm is proposed. The simulation results show that the method adopted in this paper improves the uneven image texture in art design, and its real effect is greatly enhanced.

Research on distortion quality evaluation of computer network shared image based on visual sensitivity

Shared image distortion will affect the user's experience, and then damage people's life and entertainment experience. In view of this, this research starts with the evaluation and classification of network shared image distortion quality, improves the shared image distortion quality evaluation algorithm combined with the sensitive characteristics of human vision, and verifies its performance superiority through comparative experiments. The results show that the performance of some improved reference quality evaluation algorithms reaches the highest values, which are 0.7923, 0.3224, 0.7931 and 0.8213, respectively. The improved non-reference quality evaluation algorithm achieves the highest values of positive indicators in the comparison of performance values, which are 0.487 and 0.287, respectively, while the lowest value of negative indicators is 0.902. It can be seen that the improved shared image quality evaluation algorithm conforms to the sensitive characteristics of human eyes, has high computational efficiency and has broad application prospects.

Exploring the Visual Space Structure of Oil Painting Based on Visual Importance.

For present and future design, research on visual-spatial language and the mastery of its fundamental concepts is extremely valuable. Since a more comprehensive research system has not yet been established to investigate the aesthetics of visual form and its significance, the research fields of spatial color and spatial structure relationships are currently relatively unique. The visual forms of design will be fundamentally affected by the diverse development of the design thinking ideology, and the expansion of design language will become a given. Design thinking and methods are significantly impacted by the emphasis on digital technology in online media. Design is no longer restricted to the arrangement of two-dimensional planes thanks to the visual space of oil paintings; three-dimensional space and time organization structures are combined with visual elements, and content layout evolves from static layout to dynamic interactive layout. In order to better understand visual space and intuition, this study begins by examining the structure of visual space in oil paintings. It does this by using artistic psychology and spatial space as research hints. The experimental results demonstrate that the depth-aware image quality evaluation algorithm in this study consumes more time than the MSEPF + 30 algorithm and the MSEPF + 50 algorithm by 12 ms and 15 ms, respectively, while the time consumption of individual particles is increased by 0.08 ms and 0.04 ms, respectively. This algorithm can thus meet the real-time requirements. As a result, it is anticipated that this study will address more comprehensive and detailed data as well as theoretical underpinnings that will be helpful in the study of painting art.

No Reference Image Quality Algorithm based on Human-Eye Sensitive Conception for Contrast-Distortion Evaluation

No reference image quality assessment IQAs algorithms are widely used for finding distortions in images without comparing them to references. Image' areas with no details may indicate a brightness saturation change, as well as the presence of noise in the background, which are more visible types of distortions. Thus, the design of such IQA should take into account the human visual conception. This paper proposes a no-reference image quality evaluation algorithm that takes into account the finest edge detection process and entropy deployment in regard to human visual sensitive HVS to quantify brightness saturation variations and noise in pixels. Statistic objective metrics for correlation coefficient which person PCC, spearman rank order SROCC, Root Mean Squared Error RMSE are used in the objective evaluation in corresponding with the subjective evaluation. The proposed algorithm is tested and significantly has well correlation PCC > 0.87, SROCC > 0.91, RMSE < 0.34. The findings of this research could be used to improve the performance of no-reference HVS-based IQA algorithms currently in use.

Study on image quality Control Method based on Gaussian Noise

At present, various image quality evaluation algorithms emerge in endlessly. The establishment of these algorithms, the performance evaluation of the system and the accuracy of evaluation results all need to be based on images of different quality as reference standards. This paper selects the peak signal-to-noise ratio as the characteristic parameter to measure whether the image quality changes, and uses the control image quality parameter to control the image quality. The experimental results show that the model can quickly and accurately obtain a large number of images with different distortion degrees, and control the image quality.

Three-Dimensional Image Quality Evaluation and Optimization Based on Convolutional Neural Network

Currently, three-dimensional (3D) imaging has been successfully applied in medical health, movie viewing, games, and military. To make 3D images more pleasant to the eyes, the accurate judgement of image quality becomes the key step in content preparation, compression, and transmission in 3D imaging. However, there is not yet a satisfactory evaluation method that objectively assesses the quality of 3D images. To solve the problem, this paper explores the evaluation and optimization of 3D image quality based on convolutional neural network (CNN). Specifically, a 3D image quality evaluation model was constructed, and a 3D image quality evaluation algorithm was proposed based on global and local features. Next, the authors expounded on the preprocessing steps of salient regions in images, depicted the fusion process between global and local quality evaluations, and provided the way to process 3D image samples and acquire contrast-distorted images. The proposed algorithm was proved effective through experiments.

High-Precision Lensless Microscope on a Chip Based on In-Line Holographic Imaging.

The lensless on-chip microscope is an emerging technology in the recent decade that can realize the imaging and analysis of biological samples with a wide field-of-view without huge optical devices and any lenses. Because of its small size, low cost, and being easy to hold and operate, it can be used as an alternative tool for large microscopes in resource-poor or remote areas, which is of great significance for the diagnosis, treatment, and prevention of diseases. To improve the low-resolution characteristics of the existing lensless shadow imaging systems and to meet the high-resolution needs of point-of-care testing, here, we propose a high-precision on-chip microscope based on in-line holographic technology. We demonstrated the ability of the iterative phase recovery algorithm to recover sample information and evaluated it with image quality evaluation algorithms with or without reference. The results showed that the resolution of the holographic image after iterative phase recovery is 1.41 times that of traditional shadow imaging. Moreover, we used machine learning tools to identify and count the mixed samples of mouse ascites tumor cells and micro-particles that were iterative phase recovered. The results showed that the on-chip cell counter had high-precision counting characteristics as compared with manual counting of the microscope reference image. Therefore, the proposed high-precision lensless microscope on a chip based on in-line holographic imaging provides one promising solution for future point-of-care testing (POCT).

Quality evaluation method of agricultural product packaging image based on structural similarity and MTF

With the rapid development of multimedia and communication technologies, image technology has become more and more widely used in agriculture. Aiming at the advantages of structural similarity and MTF combined algorithm, this paper proposes an image quality evaluation algorithm based on structural similarity and MTF, which mainly adopts improved gamma correction method, dual homomorphic filter correction method and illumination-independent graph method. The pre-processed image was subjected to the elimination of illumination treatment, and the experimental results of the above three methods were compared, and the experimental results of the images not subjected to illumination-independent processing were compared and analyzed. The experimental results show that the evaluation indexes of SSIM algorithm are better than other evaluation algorithms in general. The distortion in JPEG2000 image is mainly caused by the ringing effect at the edge of the image caused by wavelet transform and the blurring caused by quantization. The SSIM algorithm not only considers the edge information of the image, but also develops an illumination-invariant image recognition system, so it has obtained good evaluation results. The MATLAB research platform is used to analyze the image quality evaluation of agricultural products packaging. Through the analysis of subjective and objective evaluation results, the algorithm that combines structural similarity and MTF is superior to other algorithms.

Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy

The image quality evaluation method, based on the convolutional neural network (CNN), achieved good evaluation performance. However, this method can easily lead the visual quality of image sub-blocks to change with the spatial position after the image is processed by various distortions. Consequently, the visual quality of the entire image is difficult to reflect objectively. On this basis, this study combines wavelet transform and CNN method to propose an image quality evaluation method based on wavelet CNN. The low-frequency, horizontal, vertical, and diagonal sub-band images decomposed by wavelet transform are selected as the inputs of convolution neural network. The feature information in multiple directions is extracted by convolution neural network. Then, the information entropy of each sub-band image is calculated and used as the weight of each sub-band image quality. Finally, the quality evaluation values of four sub-band images are weighted and fused to obtain the visual quality values of the entire image. Experimental results show that the proposed method gains advantage from the global and local information of the image, thereby further improving its effectiveness and generalization.

Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting

With the rapid development of stereo image applications, there is an increasing demand to develop a versatile tool to evaluate the perceived quality of stereo images. Therefore, in this study, a blind stereo image quality evaluation (SIQE) algorithm based on convolutional network and saliency weighting is proposed. The main network framework used by the algorithm is the quality map generation network, which is used to train the distortion image dataset and quality map label to obtain an optimal network framework. Finally, the left view, right view, and cyclopean view of the stereo image are used as inputs to the network frame, respectively, and then weighted fusion for the final stereo image quality score. The experimental results reveal that the proposed SIQE algorithm can improve the accuracy of the image quality prediction and prediction score to a certain extent and has good generalization ability.

A Video Deblurring Algorithm Based on Motion Vector and An Encorder-Decoder Network

Camera shakes cause video motion blur. Video deblurring has been studied for years, and however, there are still unresolved problems, such as video frame alignment, frame selection, and frame ambiguity evaluation. We propose a video deblurring algorithm based on the motion vector and an encoder–decoder network. Our algorithm consists of four steps: first, the blurry image blocks in a video frame are located using a blurred image quality evaluation algorithm based on a response function of singular values. Second, the corresponding candidates of the blurry image block in the consecutive frames are searched using the motion vector, and the optimal candidate blocks are obtained using an objective function. Third, the blurry image block and the optimal candidate blocks are served as samples, which are inputted to an encoder–decoder network, so that the blurry image block is repaired. Finally, all blurry image blocks are replaced with the repaired ones, the boundary artifacts are eliminated, and the entire video frame is repaired. The experiments show that our algorithm yields sharper repair results, and the overall performance of our algorithm is better than other related algorithms.

Study of an image autofocus method based on power threshold function wavelet reconstruction and a quality evaluation algorithm.

As a key component in optical microscopy imaging systems, autofocus technology has a significant effect on imaging quality. In this paper, an optical microscopy autofocus method that includes a wavelet denoising algorithm based on a power threshold function and a Brenner image quality evaluation algorithm is presented. Experimental results show that the power threshold function wavelet denoising algorithm, which can be adopted to obtain more realistic optical images, is superior to the traditional soft, hard, hyperbolic, and exponential threshold functions in terms of peak signal-to-noise ratio, signal-to-noise ratio, mean squared error, and histogram indicators; moreover, compared to the Roberts, sum modulus difference (SMD), and energy gradient functions, the Brenner image quality evaluation algorithm can be used to quickly and accurately lock onto the focal plane. By integrating and applying these two core algorithms in the autofocus image acquisition system of a microscope, the image sharpness and focusing quality are greatly improved, which benefits the further evaluation of images.

Evaluation of Retinal Image Gradability by Image Features Classification

In this paper is presented a retinal image quality evaluation algorithm that classifies images into gradable and ungradable categories. The algorithm is based on the information of four retinal image quality indicators: colour, focus, contrast and illumination. Beyond being the base of the overall retinal image quality classification, these four indicators also provide important information to a fundus camera operator who can use it to better adjust the image capture process. The overall algorithm performance was evaluated through comparison against human-made classification revealing a sensitivity of 97.41% and a specificity of 99.49% in a dataset with 2032 retinal images, collated from a range of different sources, including DRIVE, Messidor, ROC and STARE datasets.

Analysis of the compression ratio and quality in aerial images

Abstract In modern photomap systems, images are stored in centralized storage. Choosing a proper compression format for the storage of an aerial image is an important problem. This paper analyses aerial image compression in popular compression formats. For the comparison of compression formats, an image quality evaluation algorithm based on the calculation of the mean exponent error value is proposed. An image quality evaluation experiment is presented. The distribution of errors in aerial images and explanation of the causes for worse than usual compression effect are analysed. An integrated solution for the aerial image compression problem is proposed and the compression format most suitable for aerial images is specified.

ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN AERIAL IMAGES

In modern photomap systems, images are stored in centralized storage. Choosing a proper compression format for the storage of an aerial image is an important problem. This paper analyses aerial image compression in popular compression formats. For the comparison of compression formats, an image quality evaluation algorithm based on the calculation of the mean exponent error value is proposed. An image quality evaluation experiment is presented. The distribution of errors in aerial images and explanation of the causes for worse than usual compression effect are analysed. An integrated solution for the aerial image compression problem is proposed and the compression format most suitable for aerial images is specified.

ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN MEDICAL IMAGES

In the modern medical information systems documents are stored in the centralized storage. Choosing a proper, for the medical image storage, compression format is an important problem. This paper analyzes medical image compression in popular compression formats. For the compression format comparison there is proposed image quality evaluation algorithm based on the calculation of the mean exponent error value. Presented image quality evaluation experiment. Analyzed the distribution of the errors in medical images and explained the causes determining better than usual compression effect. Proposed complex solution of the medical image compression problem and specified com-pression format most suitable for the medical images.