Object: To study the quantitative relationship between stimulus quantity and perception quantity of human vision for two-dimensional stimulus. Methods: Firstly, the quantitative theory of the quality perception of two-dimensional stimulus was constructed, then the method to measure the quantitative relationship between the quality of the stimulus and the quantity of the stimulus was established. Finally, the relationship between the perception quality of human vision for an image and the luminance of the image was experimentally verified. Results: Results shows the quantitative of the psychophysical perception quality for two-dimensional stimulus was realized and there is a convex and non-linear relationship between the perception quality of human vision for an image and the image luminance, and directly applied it to measure the visual quality of the image. Conclusion: The research result will lay the foundation of image quality assessment and image enhancement theory and technology as well as related industries.

The “image enhancement with luminance preservation” is a self-conflict proposition. Researchers, including propositional proposer Kim, gave the index AMBE (absolute mean luminance error) to judge if the “luminance preserving”, however, which just proved that the image enhancement with “luminance preserving” cannot be realized. We have proved that the visual quality of an image is a function of the image average luminance with convex feature. The image cannot be enhanced with luminance preservation, and the luminance cannot be preserved with image being enhanced. The visual perception quality of an image is not equal to the image contrast. We can experimentally prove what the higher the contrast of the image, the better the visual quality of the image is not true.

直至现在，图像质量评价都没有涉及色彩问题。图像质量评价的文献多是评价图像质量（在压缩、传输等图像处理过程中）的变差（降质）程度。平面图像是一种二维的亮度分布。亮度是图像视觉质量的核心参量，没有亮度就没有图像，也就没有论及图像质量的可能。本文提出了三个层次的图像视觉感知质量评价(VPQA)指标：单幅单参数图像质量评价(SS_IQA)，单幅五参数精细图像质量评价(SF_IQA)，考虑彩色保真性的增强图像质量评价(CF_IQA)。横向论，可分为单幅的图像质量评价（SIQA），多幅图像质量比较，图像增强中的质量评价等三个方面。图像视觉质量评价是智能最佳化图像增强的不可或缺的工具。

人类对刺激量的感知分为数量感知和质量感知。无论是韦伯-费克纳(Weber-Fechner)的对数感觉定律还是史蒂文斯(Stevens)的幂函数感觉定律，都是关于感觉量与一维亮度刺激之间定量关系的定律。图像属于具有二维亮度分布特征的刺激量。本文研究的是二维亮度刺激的质量的感知，即二维亮度刺激质量好坏程度的感知。好坏程度是一个模糊的心理学概念，因此我们需要用模糊数学的方法来量化图像视觉感知质量的好坏程度，即建立一个模糊隶属函数PQ来定量表示图像视觉质量的好与坏的程度。

Abstract Uterine fibroids are extremely common uterine neoplasms. However, whether robotic-assisted laparoscopic myomectomy (RALM) is superior to laparoscopic myomectomy (LM) or abdominal myomectomy (AM) is still debatable. Consequently, we aimed to compare the three currently major surgical techniques used in patients with uterine fibroids. We searched the PubMed, the Cochrane Library, MEDLINE, Embase, and Web of Science databases up to April 22, 2017. The meta-analysis included 20 studies involving 2852 patients. The number of complications [odd ratio (OR) 0.52, p = 0.009], estimated blood loss (EBL) [weighted mean difference (WMD) −33.03, p = 0.02], conversions (OR 0.34, p = 0.03), and postoperative bleeding (OR 0.18, p = 0.03) in RALM cases was significantly less than that for LM. The numbers of complications (OR 0.56, p = 0.03), length of hospital stay (WMD −1.74, p < 0.00001), EBL (WMD −77.74, p < 0.00001), and numbers of transfusions (OR 0.25, p < 0.0001) were significantly decreased, and the operative time (WMD 84.88, p < 0.00001) was significantly prolonged in RALM cases when compared to AM cases. Compared with LM and AM, RALM is associated with significantly fewer complications, significantly lower EBL, significantly fewer conversions than both LM and AM, and significantly less bleeding than LM.

Objective In a low-illumination environment,such as in nighttime video surveillance and some special scenes,the limitations of the image acquisition device,non-professional photography,and loss of information in video transmission often results in the acquisition of image with low brightness and poor contrast.Such conditions bring great challenges to image post-processing,such as image recognition,segmentation,and classification.Therefore,enhancing a low-illumination image in the preprocessing step is necessary.The aim of low-illumination image enhancement is to enhance the dark area,suppress the highlighted area,and realize the image clarity.At present,most of the methods are based on Histogram Equalization (HE),Retinex theory,and homomorphic filtering.Particularly,HE can adaptively improve the dynamic range of the image gray scale but it also can lead to unnatural over-enhancement of image contrast;and the merging gray levels cause loss of some details of the image.Retinex-based algorithms can enhance image contrast to a certain extent,but the computational complexity is generally high,the computational speed is slow,and the image color is also easily distorted.The premise of the enhancement algorithm based on homomorphic filter is that the illumination is uniform,so this method is unsuitable for low-illumination images with uneven illumination.Moreover,this method also lacks self-adaptability because the dynamic range depends on the frequency of the filter.Although the logarithmic transform can show more details of the dark area,but it also loses some details of the bright region.The Retinex algorithm and the enhancement algorithm based on homomorphic filter all involve logarithmic transformation,but none of the logarithmic base are specified.Only one-way logarithmic transformation is carried out,which can only improve the image contrast of the dark area.To overcome the shortcomings of the existing algorithms,and inspired by the characteristics of logarithmic transformation,this paper proposes a low-illumination image enhancement algorithm based on adaptive bilateral logarithm transformation with bandwidth preserving.Method The proposed method includes four steps.First,the low-illumination image is transformed into a standardized image by a special gray transformation called the standard transformation,which can stretch the image contrast to some extent.The purpose of image standard transformation is to make the image gray/color spectrum width equal to 256 to preserve full bandwidth.Compared with non-standardized image,the contrast and brightness of standardized image had increased.Standard transformation lays foundation for further image quality optimization.The second step of the algorithm is computation of the Average Luminance (AL) of the standardized image.Then,the adaptive bilateral logarithm transformation with preserved bandwidth is performed according to AL.More concretely,if AL is less than 127.5,reverse logarithm transformation with bandwidth preserving is carried out first,and then the forward logarithm transformation with bandwidth preserving is performed.Otherwise,forward logarithm transformation with bandwidth preserving is carried out,and then the reverse logarithm transformation with bandwidth preserving is performed.Through calculation,the value of logarithmic base is set to 1.021 983 956 89,thus achieving logarithm transformation with bandwidth preserving.Through this step,image details both in dark area and bright area can be displayed.Finally,the image is rounded out to obtain the enhanced image.Result In the experiment,29 high-quality images in the LIVE database release 2 are used as reference images,and then processed into low-illumination images by Photoshop CS5.After that,the proposed algorithm is utilized to enhance these low-illumination images and compared with the enhanced results obtained by HE,Multi-scale Retinex (MSR),and Natural Preserved Enhancement Algorithm (NPEA).Qualitative and quantitative analyses are conducted to evaluate the proposed algorithm.Experimental results show that the overall contrast and brightness of the proposed method are improved subjectively,and the enhancement effect is better compared with the other three enhancement algorithms.Simultaneously,the Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity (SSIM) value obtained by the proposed method is higher than the other three algorithms.The average PSNR and SSIM values obtained by the proposed method is 22.75 dB and 0.86,whereas the average PSNR and SSIM value of the other three method are 16.16 dB and0.58 (HE),15.82 dB,and0.62 (MSR),18.62 dB,and 0.78 (NPEA),respectively.In addition,the average running time of the proposed algorithm is relatively short (～74 ms);however,the running time of MSR and NPEA are respectively 11.28 s and 11.58 s under the same conditions.Conclusion The proposed method makes up for the defects of retinex algorithm and homomorphic filtering method,which can improve the dark area and bright area contrast of the image at the same time.Consequently,it can enhance the low-illumination image effectively.Moreover,the algorithm can eliminate the halo artifact caused by Retinex,and it does not merge gray levels as HE.The enhanced image is more natural and more consistent with the human visual system.Meanwhile,the proposed algorithm is simple and easy to implement,which can greatly improve the operational efficiency.The proposed method can be widely applied to image enhancement in low-illumination environment under backlight or uneven illumination.However,the limitation of the proposed algorithm is the contrast and brightness of the enhanced image should be further improved.The future work will focus on applying the average luminance transformation to the enhanced image to do further enhancement.In addition,for the sake of further improving the robustness of the algorithm,more tests and verification are required for the nighttime video monitoring field.

The crucial issue in image quality enhancement is the fidelity issue. Three fidelity criteria (3FC) are proposed in image quality enhancement. FC 1 is that the information entropy of the enhanced image should not be bigger than the original image. FC 2 is that the constituents of the enhanced image should not be bigger than the original. FC 3 that the color relationship in the enhanced image is not changed when comparing with the original. Our studies point out that the image enhancement methods based on histogram equalization and Retinex do not meet three fidelity criteria in image quality enhancement.

One of the crucial issue in image quality enhancement is the fidelity issue. There are three fidelity issues to must be studied. The 1st fidelity issue can be called that the information entropy of the enhanced image can not be increase. The 2nd fidelity issue can be called that the constituents of the enhanced image can not be increased. The 3rd fidelity issue can be called that the color relation of the enhanced image can not be changed. MSRCR will be used as an example of an image enhancement method based on the retinex series method. As an example, the three fidelity brought by multi-scale retinex with color restoration (MSRCR), which is one of series methods based on retinex, will be studied in this paper. The study will point out that the MSRCR will result in the three obvious distortions above mentioned.

提出一种快速、自适应地实现视网膜图像增强的方法.利用数学形态法和图像视觉质量的自适应变换建立适用于视网膜图像增强的方法,首先,采用数学形态法,提取图像信息,去除背景干扰;然后基于视觉的特性获取最佳的图像参数,结合Zadeh-X变换方法获取最佳图像.利用该方法所提取的视网膜图像增强了血管信息,所获图像与造影图像进行对比,优于造影图像,同时减少造影剂的使用,使病人免受造影剂的伤害.此方法可以有效地增强对比度,大大提高了图像质量,对视网膜组织进行定量分析与检测,对临床眼科学的病理诊断具有非常重要的意义.

In order to remove fog for single image fleetly ,a defogging method based on dimension reduction filter is proposed in this paper .It uses boundary constraint to achieve transmission map ,and then optimize the transmission map with dimension reduction filter .The complex computation is reduced considerably .Distortion of the image in the sky area is compensated by adaptively adjusting the atmospheric optical value and lower limit of transmission map .Then the visual effect of optimized image is improved by applying an adaptive Zadeh‐X transformation . Experimental results on a variety of haze images demonstrate real‐time performance and high‐quality of the proposed algorithm ,and it is applicable to foggy scenarios despite illumination is too low or too high and the color information is not rich enough.

在分析介质(环境)对成像质量影响的基础上,建立了介质中图像衰减模型,提出了一种同时适用于过暗、过亮、有雾和水下四种恶劣环境下的衰减补偿算法.图像的主观评价和客观评价的实验表明运用此衰减补偿算法处理后的图像拥有更好的视觉效果,并在处理速度上有较大的提升.

It is relatively difficult to identify the dynamic color images under low-light level.Therefore,in this paper,put forward is a kind of contrast resolution compensation algorithm based on human visual perception model.Firstly,a color image is transformed from RGB space into HSV space,the H elements remain unchanged.Secondly,it is to extract image feature parameters of the V element,then using contrast resolution compensate V elements so as to enhance the image brightness.Thirdly,the Selement is linearly stretched to recover the color information of the images.Finally,the treated Velements,treated Selements and untreated H elements are used to construct a new enhanced image with RGB space by inverse transform.Experimental results show that the compensation method can enhance images and improve image quality.

In order to deal with the fog image more effectively ,an improved method based on haze removal using dark channel prior is presented in this paper after analyzing its defects and shortcomings .First of all ,the original image is converted into a standard image through standardization transformation ,and then we improve speed by replacing the soft matting with guided filter to refine transmission map and adjust transmittance in bright region to avoid the distortion and halo phenomenon . The experiment results show that the improved algorithm in speed increased by 84 .2% than the original algorithm ,and refrained from distortion which caused by excessive processing in bright region ,and enhanced processing effects on color single foggy images .

Too low or too high illuminance directly impacts on the image quality.Concerning this issue,an improved adaptive optimization method of color image quality was put forward.This methord applied to image quality assessment based on four important parameters of the human visual perception about the image quality,including information entropy,average gray level,average contrast and the hierarchy factor.Then,the best transform parameter automatic optimization model had been established built on Zadeh-X transform and the optimal effect of color image was realized in different light conditions finally.The experiment results show that the quality of the processed images is significantly improved and presents better visual effect.Meanwhile,the visible detail in bright and dark regionis is increased remarkably and the image quality optimization is realized.

Under low-level-light,the invisible video images of network monitoring present the problems of low contrast and less gray information,so a method of normalized transformation is proposed based on human visual,which is inserted into the video network monitoring system based on ARM.Compared with other image enhancement methods,this algorithm can efficiently improve the contrast resolution of the image and reproduce the invisible video images,obtaining clearer and better visual effect.Meanwhile,this algorithm can meet the demand of real-time network monitoring.

In order to obtain better monitoring effect under low luminance environment,based on the human visual system,the image information was enhanced by compensating the human visual contrast resolution.Meanwhile,an embedded monitoring system was constructed with the combination of ARM11and LINUX,so the coding efficiency was greatly improved by the hardware coding.Then the encoded H264stream was transported with RTP package.Tests results show that the system can realize real-time monitoring under low luminance as well as obtain enhanced video images.

Owing to the limitations of image sensor performance from the telescopic system and the factors from the outside environment, some problems appeared in the collected remote video such as low contrast, whole partial dark and so on. Firstly, the article puts forward the evaluation method aimed at video image quality, and then the full spectrum transformation is used to the video image and the fast adaptive optimization model is established to complete fast adaptive optimization of the video image. Finally, the algorithm is embedded into the real time video to process. By comparing Comprehensive Image Quality Assessment Function(CAF), the video image through the fast adaptive optimization would have better video image quality than other video enhancement methods. Meanwhile, the experiment results prove the algorithm can meet the demand of real-time video.

Objective:With the use of gradually flattening gray/chroma spectrum and the image quality assessment to support,a new method of color image binarization was proposed.this method can be carried out image mining and hidden.it also puts forward a new 23bit color image format.Methods:The method was adopt by Zadeh-X transformation algorithm,then,applied keeping binaryzation value transformation algorithm in order to keep same mean brightness value befor and after.Results: On the basis of this method,a new image format of 23 bits was proposed yet and improved the contrust of the image.this image format can make the image information reduce,so we can use it to compress.Conclusions: The results show that the technique can make to get a better visual perception and understanding of the images,and significantly improve image quality.We also applied the theory and technique to hide image and mine out image.the new format that we proposed,can improve the contrast and make image compression.

According to the shortages existing in passively color image quality assessment currently,this paper researched the five parameters of color image feature quality perceived by human visual: average contrast,average information entropy,mean brightness(gray level),average level factor,and average bandwidth factor.On the basis of research mentioned above,the quality assessment function of color image(CAF) actively based on disturbance transform was constructed.It found that CAF was the functions of disturbance parameters Delta and Theta.Through the transformation of disturbance parameters,the overall quality evaluation function of color image would achieve the maximum value of CAF,so that the single color image quality could be assessed and improved.Several kinds of color images with narrower band,wider band and whole band spectra were assessed,and it demonstrates that the active assessment method of color image quality by considering the average bandwidth factor and average hierarchy factor conforms to the requirement of human visual subjective evaluation,and can allow the color image obtained by disturbance transformation to be more soft and more hierarchical.This method can not only evaluate the quality of single color image,but also can improve the color image quality by disturbance transformation.