STAR: A Segmentation-Based Approximation of Point-Based Sampling Milano Retinex for Color Image Enhancement

Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning

Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. How to detect tunnel cracks efficiently and avoid safety accidents caused by tunnel cracks effectively is a research hotspot at present. In order to meet the need for efficient detection of tunnel cracks, the tunnel crack detection method based on improved Retinex and deep learning is proposed in this paper. The tunnel crack images collected by optical imaging equipment are used to improve the contrast information of tunnel crack images using the image enhancement algorithm, and this image enhancement algorithm has the function of multi-scale Retinex decomposition with improved central filtering. An improved VGG19 network model is constructed to achieve efficient segmentation of tunnel crack images through deep learning methods and then form the segmented binary image. The Zhang-Suen fast parallel-thinning method is used to obtain the skeleton map of the single-layer pixel, and the length and width information of the tunnel cracks are obtained. The feasibility and effectiveness of the proposed method are verified by experiments. Compared with other methods in the literature, the maximum deviation in the length of the tunnel crack is about 5 mm, and the maximum deviation in the width of the tunnel crack is about 0.8 mm. The experimental results show that the proposed method has a shorter detection time and higher detection accuracy. The research results of this paper can provide a strong basis for the health evaluation of tunnels.

Low-Light Image Enhancement with Contrast Increase and Illumination Smooth

In image enhancement, maintaining the texture and attenuating noise are worth discussing. To address these problems, we propose a low-light image enhancement method with contrast increase and illumination smooth. First, we calculate the maximum map and the minimum map of RGB channels, and then we set maximum map as the initial value for illumination and introduce minimum map to smooth illumination. Second, we use the histogram-equalized version of the input image to construct the weight for the illumination map. Third, we propose an optimization problem to obtain the smooth illumination and refined reflectance. Experimental results show that our method can achieve better performance compared to the state-of-the-art methods.

Double-function enhancement algorithm for low-illumination images based on retinex theory

In order to solve the problems of noise amplification and excessive enhancement caused by low contrast and uneven illumination in the process of low-illumination image enhancement, a high-quality image enhancement algorithm is proposed in this paper. First, the total-variation model is used to obtain the smoothed V- and S-channel images, and the adaptive gamma transform is used to enhance the details of the smoothed V-channel image. Then, on this basis, the improved multi-scale retinex algorithms based on the logarithmic function and on the hyperbolic tangent function, respectively, are used to obtain different V-channel enhanced images, and the two images are fused according to the local intensity amplitude of the images. Finally, the three-dimensional gamma function is used to correct the fused image, and then adjust the image saturation. A lightness-order-error (LOE) approach is used to measure the naturalness of the enhanced image. The experimental results show that compared with other classical algorithms, the LOE value of the proposed algorithm can be reduced by 79.95% at most. Compared with other state-of-the-art algorithms, the LOE value can be reduced by 53.43% at most. Compared with some algorithms based on deep learning, the LOE value can be reduced by 52.13% at most. The algorithm proposed in this paper can effectively reduce image noise, retain image details, avoid excessive image enhancement, and obtain a better visual effect while ensuring the enhancement effect.

An Improved FAST Algorithm Based on Image Edges for Complex Environment

In complex environments such as those with low textures or obvious brightness changes, point features extracted from a traditional FAST algorithm cannot perform well in pose estimation. Simultaneously, the number of point features extracted from FAST is too large, which increases the complexity of the build map. To solve these problems, we propose an L-FAST algorithm based on FAST, in order to reduce the number of extracted points and increase their quality. L-FAST pays more attention to the intersection of line elements in the image, which can be extracted directly from the related edge image. Hence, we improved the Canny edge extraction algorithm, including denoising, gradient calculation and adaptive threshold. These improvements aimed to enhance the sharpness of image edges and effectively extract the edges of strong light or dark areas in the images as brightness changed. Experiments on digital standard images showed that our improved Canny algorithm was smoother and more continuous for the edges extracted from images with brightness changes. Experiments on KITTI datasets showed that L-FAST extracted fewer point features and increased the robustness of SLAM.

Adaptive Fractional Image Enhancement Algorithm Based on Rough Set and Particle Swarm Optimization

This paper proposes a new image enhancement algorithm. At first, the paper uses the combination of rough set and particle swarm optimization (PSO) algorithm to distinguish the smooth area, edge and texture area of the image. Then, according to the results of image segmentation, an adaptive fractional differential filter is used to enhance the image. Finally, the experimental results show that the image enhanced by this algorithm has clear edge, rich texture details, and retains the information of the smooth area of the image.

An Image Contrast Measure Based on Retinex Principles

The image contrast is a feature capturing the variation of the image signal across the space. Such a feature is very useful to describe the local image structure at different scales and thus it is relevant to many computer vision applications, like image/texture retrieval and object recognition. In this work, we present MiRCo, a novel measure of image contrast derived from the Retinex theory. MiRCo is robust against in-plane rotations and light changes at multiple scales. Thanks to these properties, MiRCo enables an accurate and robust description of the local image structure. Here we describe and discuss the mathematical insights of MiRCo also in comparison with other popular contrast measures.

Joint Retinex-based variational model and CLAHE-in-CIELUV for enhancement of low-quality color retinal images

Poor visual quality of color retinal images greatly interferes with the analysis and diagnosis of the ophthalmologist. In this paper, we propose an enhancement method for low-quality color retinal images based on the combination of the Retinex-based enhancement method and the contrast limited adaptive histogram equalization (CLAHE) algorithm. More specifically, we first estimate the illumination map of the entire image by constructing a Retinex-based variational model. Then, we restore the reflectance map by removing the illumination modified by Gamma correction and directly enable the reflectance as the initial enhancement. To further enhance the clarity and contrast of blood vessels while avoiding color distortion, we apply CLAHE on the luminance channel in CIELUV color space. We collect 60 low-quality color retinal images as our test dataset to verify the reliability of our proposed method. Experimental results show that the proposed method is superior to the other three related methods, both in terms of visual analysis and quantitative evaluation while testing on our dataset. Additionally, we apply the proposed method to four publicly available datasets, and the results show that our methods may be helpful for the detection and analysis of retinopathy.

Generalized equation for real-world image enhancement by Milano Retinex family

Milano Retinexes are spatial color algorithms grounded on the Retinex theory and widely applied to enhance the visual content of real-world color images. In this framework, they process the color channels of the input image independently and re-scale channel by channel the intensity of each pixel p by the so-called local reference white, i.e., a strictly positive value, computed by reworking a set of features sampled around p. The neighborhood of p to be sampled, its sampling, the features to be processed, as well as the mathematical model for the computation of the local reference white vary from algorithm to algorithm, determining different levels of enhancement. Based on the analysis of a group of Milano Retinexes, this work proves that the Milano Retinex local reference whites can be expressed by a generalized equation whose parameters model specific aspects of the Milano Retinex spatial color processing. In particular, tuning these parameters leads to different Milano Retinex implementations. This study contributes to a better understanding of the similarities and differences among the members of the Milano Retinex family, and provides new taxonomic schemes of them based on their own mathematical properties.

SuPeR: Milano Retinex implementation exploiting a regular image grid

A spatial color algorithm grounded on the Retinex theory is known as a Milano Retinex. This type of algorithm performs image enhancement by processing spatial and color cues in the neighborhood of each image pixel. Because this local, pixel-wise analysis is time consuming, optimization techniques are needed to expand the use of Milano Retinexes to applications that require fast or even real-time image processing. In this work, we propose SuPeR, an efficient optimization of the Milano Retinex local spatial color processing that exploits superpixels, which are as the regular, rectangular blocks of a grid that partitions the image support. Image enhancement is obtained by reworking channel-wise the intensity of each pixel based on the maximum color intensities of the blocks and on its distance from the blocks. The experiments, carried out on real-world image datasets, show good performance compared to other Milano Retinexes.