Edge 115 with Enhance Images option missing in . Based on this theory, many scholars have proposed image enhancement algorithms for specific applications based on PCNN [103,104,105,106]. [143]. Image processing is the process of transforming an image into a digital form and performing certain operations to get some useful information from it. [145] thought that the traditional multi-scale Retinex (MSR) [63] algorithm can be regarded as a feedforward convolutional neural network with different Gaussian convolution kernels. Importance of Image Enhancement in Graphic Design Image enhancement plays an important role in improving image quality in the field of image processing, which is achieved by highlighting useful information and suppressing redundant information in the image. Pattern Recognit Lett 36:1014, Land EH (1977) The retinex theory of color vision. Neural Netw 61:85117, Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. In addition, a non-overlapped sub-blocks and local histogram projection (NOSHP) is presented by Liu et al. The basic PCNN is elaborated in Ranganath et al.s work [96]. Therefore, the first firing time \({T_{ij}}\) can be obtained from (34). 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. The histogram frequency weighting technique considers the relationship between histogram equalization and image gray-scale frequency. bf are the enhancement results by PCNN, SCM, FLM, LSCN and HRYNN, respectively. \({C_j}\), \({H_j}\) and \({W_j}\) are the number, height and width of the feature maps, respectively. Then, the experimental evaluation of deep learning based image enhancement methods is conducted. 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. or less than j, and T is the total number of pixels.The main purpose of histogram equalization is to find gray level transformation function T to transform image f such that the histogram of T(f) is equalized. MATH 68496857, Wang W, Wei C, Yang W, Liu J, Gladnet: Low-light enhancement network with global awareness, In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), IEEE, 2018, pp. IEEE Trans Image Process 19(11):28252837, Ma W,Morel JM, Osher S, Chien A (2011) An l1-based variational model for retinex theory and its application to medical images, In: The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, USA, 20-25 June 2011, Osher S, Burger M, Goldfarb D, Xu J, Yin W (2005) An iterative regularization method for total variation-based image restoration. 16321640, Shen L, Yue Z, Feng F, Chen Q, Liu S, Ma J (2017) Msr-net: Low-light image enhancement using deep convolutional network, arXiv preprint arXiv:1711.02488, Wei C, Wang W, Yang W, Liu J (2018) Deep retinex decomposition for low-light enhancement, arXiv preprint arXiv:1808.04560, Shi Y, Xiaopo W, Zhu M (2019)Low-light image enhancement algorithm based on retinex and generative adversarial network, arXiv preprint arXiv:1906.06027, Wang Y, Cao Y, Zha Z-J, Zhang J, Xiong Z, Zhang W, Wu F (2019) Progressive retinex: Mutually reinforced illumination-noise perception network for low-light image enhancement, In: Proceedings of the 27th ACM International Conference on Multimedia, ACM, 2019, pp. [87], LR3M is full aware of noise and performs adaptive processing throughout the enhancement process. They injected low-rank priors into the Retinex decomposition process for the first time and suppressed noise in the reflection map. In: Proceedings of the IEEE International Conference on Computer Vision Workshops 2017:30153022, Wang R, Zhang Q, Fu C-W, Shen X, Zheng W-S, Jia J (2019) Underexposed photo enhancement using deep illumination estimation, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. According to [9], SSR model can be described as: where \({I_i}\) is the image distributed in the i-th color band, \({R_i}(x,y)\) is the enhancement result, * means convolution operation, F(x,y) represents the convolution kernel function and the formula is as follows: where k needs to satisfy \(\iint \limits F(x,y) \mathrm {d}x \mathrm {d}y=1\). The larger the value of SSIM, it means that the structure of the enhanced image is similar to the original image and the quality of the enhanced image is better. Motivated by this fact, Qi et al. 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. Similar to WHE, Wong et al. \(\phi\) denotes the time constants of inertial block and h represents the threshold of summarizing element. The spatial image enhancement [2] is to directly process the pixels in the image, such as the classic modified histogram methods [3,4,5], the improved unsharp mask methods [6,7,8]. Look it up now! Some examples of Retinex algorithm enhancement based on variational methods.a is the original image. 8. Visualize the Future, IEEE, pp 3743, Lindblad T, Kinser JM, Taylor J (2005) Image processing using pulse-coupled neural networks. In addition to the above enhancement algorithm, Zhan et al. IEEE Trans Consum Electron 53(2):593600, Ibrahim H, Kong NSP (2007) Brightness preserving dynamic histogram equalization for image contrast enhancement. What is the use of image enhancement? 2 in this paper. Their model is described as follows: where \({\left\| {\nabla {{\mathcal {B}}}} \right\| _2^{}}\) is the TV term, which is used to obtain a piecewise smooth reflection map, t is the weight coefficient. MSR-net consists of three modules: multi-scale logarithmic transformation, convolution difference and color restoration. In their method, noise can be suppressed to a certain extent. Section4 describe in detail the quality evaluation content of image enhancement. \({F^S}\) 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. Finally, to effectively decompose the illumination and reflectivity, the MAP problem is transformed into an energy minimization problem. 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. In this section, we will give a detailed overview of the qualitative and quantitative evaluation for image enhancement. Image Enhancement: A Review | SpringerLink J Test Meas Technol 19(3):304309, Li G-Y, Li H-G, Wu T-H (2005) Enhancement of image based on otsu and modified pcnn [j]. As we know, low-quality images often exhibit low contrast, artifacts, and noise due to some extreme conditions. Archives of Computational Methods in Engineering Here we briefly summarize. Typical methods are brightness preserving bi-histogram equalization [35] (BBHE), dualistic sub-image histogram equalization [4](DSIHE), minimum mean brightness error bi-histogram equalization [36] (MMBEBHE). [119] proposed a RYNN model by introducing the redefined threshold segmentation module and a nonlinear generator. In this paper, we give a comprehensive review to analyze image enhancement methods from a supervised and unsupervised perspective. In addition to the human eyes method of perceiving brightness, namely (32), the time matrix is also one of the most important tools for image enhancement. In addition, the receptive field of the \(1 \times 1\) convolution kernel is relatively small, resulting in no neighborhood information during convolution. Therefore, some researchers began to work on weakly supervised or unsupervised deep learning methods for low-light image enhancement. However, subjective evaluation methods lack stability. IEEE Trans Image Process A Publ IEEE Signal Process Soc 22(9):35383548, Kellman P, McVeigh ER (2005) Image reconstruction in snr units: a general method for snr measurement. [53] used a random midpoint displacement method so that the path chosen is close to Brownian motion. Image enhancement based on histogram specification. We first survey the unsupervised image enhancement methods, including histogram specification, Retinex model, deep learning and visual cortex neural network. In image enhancement, an appropriate quality evaluation strategy plays a key role in the evaluation of algorithm performance. Image enhancement is considered as one of the most important techniques in image research. [127] proposed a unpaired learning method based on GAN. Target attention deep neural network for infrared image enhancement The evaluated approaches include Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), partially overlapped sub-block histogram equalization(POSHE) [27], WTHE [29] and AGCWD [33], SRIE [85], LIME [86], SCM [113] and HRYNN [119]. Path-based Retinex algorithm can effectively improve the image contrast. Bedi SS, Khandelwal R (2013) Various image enhancement techniques-a critical review. Edge Enhancement in Indoor Digital Images | Oriental Journal of 6e. Shen et al. 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. The purpose of the image enhancement is to improve the visual interpretability of an image by increasing the apparent distinction between the features in the scene. Then, structural similarity of image (SSIM) is used to evaluate the similarity of two images and is proposed by Wang et al. [87] proposed a Retinex model with a definite injected noise term, and for the first time tried to estimate the noise map based on the model. Therefore, the formation of a low-light image can be described as follows: where L(x,y) is the original image, R(x,y) is the reflection image, B(x,y) is the illuminance image and (x,y) is the pixel coordinates. PDF Importance of Image Enhancement Techniques in Color Image Segmentation In Qi et al.s work, input receptive field \({F_{ijkl}^S}\) is refined as follows: where \({a_1}\) and \({a_2}\) are the distribution sensitivity. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China, Yunliang Qi,Zhen Yang,Meng Lou,Wenwei Zhao&Yide Ma, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, Gansu, China, The College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730070, China, You can also search for this author in Since then, some novel models have been constructed based on the Kimmel et al.s model by modifying or adding some constraints [79,80,81], which reflects the flexibility of the variational method. The threshold \({E_{ij}}\) is dynamic, because when the neuron is activated, the threshold would increase by the amplification item \({\mathrm{{V}}_E}\). In the work of Provenzi et al. \(\sigma\) represents kernel parameter, which can determine the enhancement effect of image. [52] proved that the threshold mechanism has very little contribution to the model algorithm, so (5) can be simplified as: For the path-based Retinex algorithm, the researchers have focused on the selection of the pixel path, and lots of related work has gradually emerged. There are three main aspects. In addition, adversarial color loss \({{\mathcal {L}}}_{color}\) is defined to compare enhanced image and high-quality image. In this section, we mainly elaborate three typical image enhancement methods based on histogram modification: traditional histogram equalization, partial histogram equalization and histogram frequency weighting, respectively. In 2016, related work of [85] proposed a weighted variational model for simultaneous reflectance and illumination estimation (SRIE) with more details. Finally, the activation state of the neuron is determined, that is, the output \(Y_{ij}\) is 1 (activated) or 0 (not activated). 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. The feedback input \({F_{ij}}\) and linking input \({L_{ij}}\) are combined with a link factor \(\beta\) to obtain an internal activity \({U_{ij}}\). The logarithmic transformation characteristic of the image is shown in Fig. Specially, a structure prior is imposed to refine the illumination map. Typical operations include filtering with morphological operators, histogram-based equalization, brightness, and contrast adjustment (Acharya and Ray, 2005 ). Signal Process Image Commun 58:187198, Qiuqi R, Yuzhi R (2013) Digital Image Processing, 3rd edn. 10. Further, the pulse coupled neural network (PCNN) evolved from the Eckhorn model has been extensively and deeply studied by researchers. Applied Sciences . Note that EME is highly sensitive to noise. However, due to the uncertainty of its initial position, end position and path selection, it is easy to introduce undesirable noise and affect the accuracy of illumination estimation. IEEE Trans Image Process A Publ IEEE Signal Process Soc 22(2):657667, Zhang R, Isola P, Efros AA, Shechtman E,Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric, In: IEEE/CVF Conference on Computer Vision & Pattern Recognition, Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J (2016) A fusion-based enhancing method for weakly illuminated images. Jiang et al. The threshold exponential decay coefficient \({{\alpha }_e}\) is a small value and is set to 0.001. \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;(m = 1,2,3) \end{array} \end{aligned} \end{aligned}$$, $$\begin{aligned}&Z = {Z_1} + {Z_2} + {Z_3} \end{aligned}$$, $$\begin{aligned} {L_{12}} = {\beta _{12}}\sum {{W_{ijkl}}(1,2){Z_{1,kl}}(n - 1)} \end{aligned}$$, $$\begin{aligned} {L_{23}} = {\beta _{23}}\sum {{W_{ijkl}}(2,3){Z_{2,kl}}(n - 1)} \end{aligned}$$, $$\begin{aligned} {W_{ijkl}}(1,2) = {W_{ijkl}}(2,3) = \frac{1}{{{{(i - k)}^2} + {{(j - l)}^2}}} \end{aligned}$$, \(\mathop x\limits ^ \sim = F(\mathop y\limits ^ \sim ) = F \circ G(x)\), $$\begin{aligned} {{\mathcal {L}}}_{content}= \frac{1}{{{C_j}{H_j}{W_j}}}\left\| {{\varPsi _j}(x) - {\varPsi _j}(\mathop x\limits ^ \sim )} \right\| \end{aligned}$$, $$\begin{aligned} {{\mathcal {L}}}_{color}= & {} - \sum \limits _i {\log {D_c}(G{{(x)}_b})} \end{aligned}$$, $$\begin{aligned} {{\mathcal {L}}}_{texture}= & {} - \sum \limits _i {\log {D_t}(G{{(x)}_g})} \end{aligned}$$, $$\begin{aligned} {{\mathcal {L}}}_{tv} = \frac{1}{{CHW}}\left\| {{\nabla _x}G(x) + {\nabla _y}G(x)} \right\| \end{aligned}$$, $$\begin{aligned} AMBE(X,Y) = \left| {MB(X) - MB(Y)} \right| \end{aligned}$$, $$\begin{aligned} SSIM(x, y) = l{(x,y)^\alpha } \cdot c{(x,y)^\beta } \cdot s{(x,y)^\gamma } \end{aligned}$$, $$\begin{aligned} l(x,y)&= \frac{{2{\mu _x}{\mu _y} + {c_1}}}{{\mu _x^2 + \mu _y^2 + {c_1}}},\quad c(x,y) = \frac{{2{\sigma _x}{\sigma _y} + {c_2}}}{{\sigma _x^2 + \sigma _y^2 + {c_2}}},\nonumber \\ s(x,y)&= \frac{{{\sigma _{xy}} + {c_3}}}{{\sigma _x^{}\sigma _y^{} + {c_3}}} \end{aligned}$$, \(\mathrm{{SSIM(x,y)}} \in \mathrm{{(0,1)}}\), $$\begin{aligned} PSNR = 10{\log _{10}}(\frac{{peakval{^2}}}{{MSE}}) \end{aligned}$$, $$\begin{aligned} MSE = \frac{1}{{mn}}\sum \limits _{i = 0}^{m - 1} {\sum \limits _{j = 0}^{n - 1} {{{\left\| {I(i,j) - {I_0}(i,j)} \right\| }^2}} } \end{aligned}$$, $$\begin{aligned} DE(X) = - \sum \limits _{i = 0}^{255} {p({x_i})\log p({x_i})} \end{aligned}$$, $$\begin{aligned} EME(X) = \frac{1}{{{k_1}{k_2}}}\sum \limits _{i = 1}^{{k_1}} {\sum \limits _{j = 1}^{{k_2}} {20In\frac{{\max ({X_{ij}})}}{{\min ({X_{ij}})}}} } \end{aligned}$$, $$\begin{aligned} L(x,y) = \mathop {\max }\limits _{c \in \{ r,g,b\} } {I^c}(x,y) \end{aligned}$$, $$\begin{aligned} RD(x,y) = \nonumber \\ \sum \limits _{i = 1}^m {\sum \limits _{j = 1}^n {(U(L(x,y),L(i,j)) \oplus U({L_e}(x,y),{L_e}(i,j)))} } \end{aligned}$$, $$\begin{aligned} LOE = \frac{1}{{m*n}}\sum \limits _{i = 1}^m {\sum \limits _{j = 1}^m {RD(i,j)} .} [30] performed histogram equalization with maximum intensity coverage. And by using a different kinds of image enhancement techniques, such as artificial intelligence techniques methods . Many state-of-the-art algorithms have been developed for this purpose. Different image enhancement techniques exist in the literature. Note \(\mathrm{{SSIM(x,y)}} \in \mathrm{{(0,1)}}\). Finally, WESPE loss is defined as the linear weighting of the four component losses. That is to say, AHE is more suitable for improving the local contrast of the image and obtaining more image details. Representative methods include mean square error (MSE) and peak signal-to-noise ratio (PSNR). Note that in the Table1 below, blod and underlined indicate the best and second place results, respectively. From (35) we get the analytical solution of the time matrix \({T_{ij}}\), but \({T_{ij}}\) still cannot be obtained because (35) is an implicit function. Edge enhancement is a pre-processing technique for all images. It is planned to perform automatic edge enhancement in indoor digital images. Qi, Y., Yang, Z., Sun, W. et al. IEEE Trans Consum Electron 43(1):18, Der Chen S, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. In Qi et al.s work, HRYNN is described as follows: where m denotes m-th RYNN subcell. In addition, the Mach zone phenomenon is explained through experiments and the rationality of the method is established. In 2019, Nie et al. Double histogram equalization is to decompose the original histogram into two histograms, and then equalize the two histograms separately. Kimmel et al. 691700, Aly HA, Dubois E (2005) Image up-sampling using total-variation regularization with a new observation model. For example, eliminating noise, revealing blurred details, and adjusting levels to highlight features of an image. A Review of Image Enhancement Techniques in Medical Imaging Fu et al. If I represents the original image and \({I_e}\) represents the enhanced image, the lightness of the image is represented by the highest brightness among the three channels: For each pixel, before and after image enhancement, the lightness order error related to that pixel is defined as follows: where m and n represent the height and width of the image respectively.

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