importance of image enhancementdivinity 2 respec talents

In their work, firstly, supervised learning is employed to obtain the characteristics of reconstructed high-quality images. However, they do not need to correspond in content. Each image enhancement method has its own advantages and disadvantages. The purpose of image enhancement and image restoration techniques is to perk up a quality and feature of an image that result in improved image than the original one. In other words, the neural network receives the higher intensity pixel input in the image and returns a smaller value in the time matrix. Int J Comput Vision 83(1):101119, Land EH (1986) An alternative technique for the computation of the designator in the retinex theory of color vision. Comput Med Imaging Graph 24(2):5968, Kaur A, Singh C (2017) Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. They considered the gamma band oscillations in visual cortical neurons as the key role to improve the model. Similar to WHE, Wong et al. Lamberti et al. In their work, the input image is first denoised by PCNN, and then segmented by PCNN, and the continuous output of multiple binary images is logically OR and exclusive or (EOR) operations to obtain image texture, edge and other information. Publishing House of Electronics Industry, Le-Peng LI, Sun SF, Xia C, Chen P, Dong FM (2014) Survey of histogram equalization technology. b, c and d are the enhanced results of POSHE [27], WTHE [29] and AGCWD [33], respectively. a is the original image. Some examples of Retinex algorithm enhancement based on center/surround.a is the original image. Bell Syst Tech J 5(3):355, Agaian SS, Silver B, Panetta KA (2007) Transform coefficient histogram-based image enhancement algorithms using contrast entropy. Li et al. Firstly, most of the predictions are performed under a low-resolution bilateral grid; Secondly, the entire neural network structure learns an affine transformation; Thirdly, most of the operations are performed at low resolution, but the loss function is finally established on the original resolution, in other words, the original resolution image can be optimized based on low resolution operations. IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018:32913300, Hu Y, He H, Xu C, Wang B, Lin S (2018) Exposure: a white-box photo post-processing framework. However, the parameter settings in the algorithm have limitations, resulting in uncertainty in the contrast, chroma, and clarity of the enhanced image, so the enhancement effect is not ideal. The basic idea of the algorithm is to sum multiple Gaussian kernel scale parameters by weight and its expression is as follows: where \({F_m}(x,y)\) is the filter kernel function, and the scale and the total number of scales are \({\delta _m}\) and N respectively. The other is to convert the image to a brightness channel in the color model (such as HSV color model, CIELuv color model) and its brightness channel is processed separately to solve the problem of uneven illumination. Section4 describe in detail the quality evaluation content of image enhancement. The loss function is the F-norm square of the error matrix with regular terms. 12 that AHE and SRIE have better performance in detail enhancement, but HE, WTHE and AGCWD cause serious loss of details, such as the edge of a teacup, etc. Zamir et al. The quality evaluation of image processing algorithms is essential, which involve peoples evaluation of the visual perception of an image. 844848, Yang X, Liu T, Li X (2016) Study on image enhancement algorithm merged wavelet transform and improved pcnn [j]. Therefore, the time matrix of PCNN can be obtained by the following Algorithm 2. In this paper, we give a comprehensive review to analyze image enhancement methods from a supervised and unsupervised perspective. In their researches, the original image first is divided into lots of non-overlapping sub-blocks, and then histogram projection (HP) is performed respectively. Abstract. The Retinex theory was proposed by Land et al. It is the purpose of adjusting digital images so that the results are more suitable for display or further image analysis. Representative methods include mean square error (MSE) and peak signal-to-noise ratio (PSNR). Similarly, they also proposed a L1-Retinex model [74], which is expressed as follows: In summary, the PDE model constructs partial differential equations based on some basic assumptions of Retinex theory, and its description is more accurate than the path-model. However, histogram equalization is a method to adjust the image globally, which cannot effectively improve the local contrast, and the effect is very poor in some situations. Image enhancement [ 1] is a subfield of digital image processing. IEEE Trans Image Process 27(4):20492062, Lv F, Li Y, Lu F (2019) Attention guided low-light image enhancement with a large scale low-light simulation dataset, arXiv: 1908.00682, Zamir SW, Arora A, Khan S,Hayat M, Khan FS, Yang M-H, Shao L (2020) Learning enriched features for real image restoration and enhancement, arXiv preprint arXiv:2003.06792, Zhu M, Pan P, Chen W, Yang Y (2020) Eemefn: Low-light image enhancement via edge-enhanced multi-exposure fusion network. MathSciNet Step 2: Obtain the time matrix \({T_{ij}}\) by Algorithm 2. SYD is a large scale low-light simulation dataset with 22,656 scenes, which has diverse scenes and lighting conditions. This is mainly caused by the following factors: the non-university of image enhancement algorithm, the choice of evaluation index, the influence of noise and the choice of optimal parameters, etc. We compare various image enhancement methods on randomly selected low-brightness test images. IEEE Signal Process Lett 20(12):12401243, Banic N, Loncaric S (2015) Smart light random memory sprays retinex: a fast retinex implementation for high-quality brightness adjustment and color correction. [71, 72] derived a similar Poisson equation based on the path-based Retinex model. The Retinex-like model has the disadvantage of a large calculation. [83] proposed a probabilistic model for image enhancement (PIE). In this section, we first elaborates the quantitative evaluation index for the enhanced image. IEEE Trans Image Process 16(1):162171, Banic N, Loncaric S (2013) Light random sprays retinex: exploiting the noisy illumination estimation. [128] proposed an EnlightenGAN method for deep light enhancement. Based on Rybaks theory, Qi et al. First of all, according to literature [153], the absolute mean brightness error(AMBE) is defined as: where MB(X) and MB(Y) represent the mean brightness of original image X and enhanced image Y, respectively. Specifically, the greater the grayscale difference from the standard image, the more severe the image quality degradation. In addition, they proved that the Retinex model can be characterized as a discrete Poisson equation with Neumann boundary conditions and expressed as follows: where \({\varDelta _d}\) and \({\partial _d}\) represent the discrete Laplace operator and discrete partial differential symbols respectively. Note that implementation is done with Keras and Tensorflow. J Opt Soc Am A Opt Image Sci Vision 32(11):21362147, Bertalmio M, Caselles V, Provenzi E (2009) Issues about retinex theory and contrast enhancement. It is planned to perform automatic edge enhancement in indoor digital images. 6e. In order to preserve the brightness characteristics of the image and better enhance the local details of the image, many scholars have begun to devote themselves to the research of local histogram equalization(LHE) algorithms [4, 24,25,26]. They constrain the power consumption by reducing the brightness and the perceived visual quality is preserved by CNN enhancement. Lv et al. Therefore, the first firing time \({T_{ij}}\) can be obtained from (34). Optik Int J Light Electron Opti 27(1):407414, Liu L, Jia Z, Yang J, Kasabov N (2015) A medical image enhancement method using adaptive thresholding in nsct domain combined unsharp masking. Generally, Retinex performs image processing based on two methods: one is to divide the color image into three RGB channels and the Retinex algorithm is applied to each channel to solve the color shift problem. IEEE Trans on Image Process 29:72177232, Eckhorn R, Reitboeck H, Arndt M, Dicke P (2014) Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Acta Biophysica Sinica 04:65, Qi Y, Yang Z, Lian J, Guo Y, Sun W, Liu J, Wang R, Ma Y (2021) A new heterogeneous neural network model and its application in image enhancement. Inspired by this fact, many scholars have proposed noise suppression low-brightness image enhancement methods based on the Retinex model. In implementation, the enhancement image can be obtained by the negative of the time matrix. However, if the input image is severely overexposed and the area is large, CNN can use little neighborhood information. IEEE Trans Image Process A Publ IEEE Signal Process Soc 6(3):451462, Jobson DJ, Rahman Z, Woodell GA (2002) A multiscale retinex for bridging the gap between color images and the human observation of scenes. In addition, \(\alpha\), \(\beta\) and \(\gamma\) are parameters employed to adjust the relative importance of the three components. In addition, we also provide main quality evaluation methods for image enhancement. [87] and Tang et al. Other parameters have the same meaning as the basic Rybak model. Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study Dibya Jyoti Bora Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. [149] proposed a deep learning method for low-light images enhancement. Image enhancement is considered as one of the most important techniques in image research. Procedia Comput Sci 115:509517, Wang Q, Ward RK (2007) Fast image/video contrast enhancement based on weighted thresholded histogram equalization. This work is jointly supported by the National Natural Science Foundation of China (No.62061023 and 61961037), the Natural Science Foundation of Gansu Province (No.18JR3RA288) and the Fundamental Research Funds for the Central Universities of China (No.lzujbky-2017-it72). The feedback input \({F_{ij}}\) and linking input \({L_{ij}}\) are combined with a link factor \(\beta\) to obtain an internal activity \({U_{ij}}\). 2760, International Society for Optics and Photonics, pp. ACM Trans Graph (TOG) 36(4):112, Lv F, Lu F, Wu J, Lim C (2018) Mbllen: Low-light image/video enhancement using cnns., In: BMVC, p. 220, Cai J, Gu S, Zhang L (2018) Learning a deep single image contrast enhancer from multi-exposure images. IEEE Trans Image Process 18(9):19211935, Yun SH, Jin HK, Kim S (2011) Contrast enhancement using a weighted histogram equalization. Pattern Recognit Lett 24(11):16631677, Provenzi E, De Carli L, Rizzi A, Marini D (2005) Mathematical definition and analysis of the retinex algorithm. Yang et al. The evaluated image enhancement methods cover BIMEF [164], LIME [86], MSRCR [10], MF [163], SRIE [85], Dong [165], NPE [157] , DHECI [166], BPDHE [40], HE, Ying [168], WAHE [31], JED [167], Robust [87], LLNet [171], DeepUPE [169], GLADNet [170], MBLLEN [139] and Lv [141]. We first survey the unsupervised image enhancement methods, including histogram specification, Retinex model, deep learning and visual cortex neural network. Proc Natl Acad Sci U S Am 80(16):51635169, Brainard DH, Wandell BA (1986) Analysis of the retinex theory of color vision. A Comprehensive Overview of Image Enhancement Techniques. IEEE Trans on Neural Netw 22(6):880892, Ranganath H, Kuntimad G, Johnson J (1995) Pulse coupled neural networks for image processing. in Fig. IEEE Trans Image Process 16(3):741758, Article Compared with the logarithmic domain processing algorithm, PIE has the advantages of preserving details well and avoiding over-smooth reflection components. MSR-net consists of three modules: multi-scale logarithmic transformation, convolution difference and color restoration. \({F^S}\) is a local connection of on-center/off-surround and \({F^I}\) is a local oriented connection, which exist in the visual cortex neural networks with a large number of different resolutions. It is difficult to propose a method that has a good enhancement for all images. In summary, the local histogram method performs local equalization processing for different spatial location regions, and fully takes into account the local information of the image. Image enhancement refers to operations aimed at adjusting digital images to improve display and facilitate further analysis for the extraction of quantitative information. In addition, supervised methods such as LLNet [171], DeepUPE [169], GLADNet [170], MBLLEN [139] and Lv [141] basically achieved a better performance. The third item \({{{\left| {\nabla \left( {{{\mathcal {B}}} - {{\mathcal {L}}}} \right) } \right| }^2}}\) is equivalent to \({{{\left| {\nabla \left( {{\mathcal {R}}} \right) } \right| }^2}}\), and its function is to obtain the spatial distribution of the reflected components as smooth as possible, so as to obtain a better visual effect. Neural Comput 2(3):293307, Reitboeck HJ (1989) A model for feature linking via correlated neural activity, Synergetics on Cognition, Parodi O, Combe P, Ducom JC (1996) Temporal coding in vision: coding by the spike arrival times leads to oscillations in the case of moving targets. In this paper, we comprehensively provide the progress of image enhancement technology in the past two decades. 2021; 11(18):8388. https://doi.org/10.3390/app11188388 The input \({S_{ij}}\) is only included in the feedback input \({F_{ij}}\). 63066314, Jiang Y, Gong X, Liu D, Cheng Y, Fang C, Shen X, Yang J, Zhou P, Wang Z (2019)Enlightengan: Deep light enhancement without paired supervision, arXiv preprint arXiv:1906.06972, Xiong W, Liu D, Shen X, Fang C, Luo J (2020) Unsupervised real-world low-light image enhancement with decoupled networks, arXiv preprint arXiv:2005.02818, Yang W, Wang S, Fang Y, Wang Y, Liu J (2020) From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement, In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. In addition, Cai et al. This study, based on the proposed method of average gradient expression, improves the contrast of objects and enhances the edge information pertaining to objects relative to other methods and . POSHE reduces the blocking effect caused by partial equalization and simplifies the number of equalizations. Neurocomputing 440:336350, Huang Y, Ma Y, Li S (2015) A new method for image quantization based on adaptive region related heterogeneous pcnn, In: International Symposium on Neural Networks, Huang Y, Ma Y, Li S, Zhan K (2016) Application of heterogeneous pulse coupled neural network in image quantization. In addition, SCM seems to have an artifact effect and LIME cause a illumination over-enhancement, which adds to the noise amplification. bf are the enhancement results by PCNN, SCM, FLM, LSCN and HRYNN, respectively. The frequency domain image enhancement is to convert the image to the frequency domain in a certain mathematical function such as Fourier transform (FT), discrete cosine transform (DCT) and discrete wavelet transform (DWT), then perform image processing based on the unique properties of the frequency domain, and finally convert it to the original image space. IEEE Trans Consum Electron 52(3):966974, Liu B, Jin W, Chen Y, Liu C, Li L (2011) Contrast enhancement using non-overlapped sub-blocks and local histogram projection. There are many other image enhancement methods based on histogram modification that have not been listed, such as double histogram equalization, dynamic sub-histogram equalization, transform domain equalization and histogram clipping. Then EME can be computed as. High contrast sub-blocks give a high EME value and the homogeneous sub-blocks can result in an EME value close to 0. The performance can be improved by the following strategies: designing novel optimization loss, modifying channel attention mechanism, Retinex combination, adapting scale-aware enhancement, and learning multi-level fused CNN features. Google Scholar, Arici T, Dikbas S, Altunbasak Y (2009) A histogram modification framework and its application for image contrast enhancement. The enhancement technique is used to extract the details of images that are obscured or to highlight the important features of images that are of due importance while applying the images in a field. Section3 elaborates the supervised algorithms for image enhancement. Contrast is an important factor in any subjective evaluation of image quality. Neural Netw 4(1):313, Chen Y, Park S-K, Ma Y, Ala R (2011) A new automatic parameter setting method of a simplified pcnn for image segmentation. In addition, (27) is usually simplified to \({F_{ij}}[n] = {S_{ij}}\). IEEE Trans Neural Netw 10(3):5918, Ranganath HS, Kuntimad G (1996) Iterative segmentation using pulse-coupled neural networks, In: Applications and Science of Artificial Neural Networks II, Vol. J Modern Opt 63(15):14441450, Abdullahalwadud M, Kabir H, Dewan MAA, Chae O (2007) A dynamic histogram equalization for image contrast enhancement. In order to reduce the color cast and restore the true color of the image, a multi-scale Retinex algorithm with color restoration factor(MSRCR) [10, 11, 64] was proposed. [130] proposed a semi-supervised method for low-light image enhancement. \(\sigma\) represents kernel parameter, which can determine the enhancement effect of image. Therefore, multi-level features fusion is an effective method in the enhancement of low-light images. In addition, the PDE model has fewer parameters and the results obtained are more stable. We will introduce in detail as follows: The whole framework of image enhancement methods. In 2018, Ignatov et al. Signal Process Image Commun 58:187198, Qiuqi R, Yuzhi R (2013) Digital Image Processing, 3rd edn. In Gaberiel et al.s work, a large dataset of HDR images is collected to train CNN. Its basic idea is to use the local brightest point in the path-White Patch [51] (WP) to calculate the relative brightness of adjacent pixels in the path to obtain the reflection component. Although the Retinex algorithms based on center/surround have a good effect in terms of color fidelity, the time complexity is high, the halo phenomenon is obvious and image distortion is inevitable.

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