Improved Retinex Algorithm Research Articles

Remote sensing image dehazing method in mountaineering equipment

Abstract With the spread of national sports awareness, more people are fond of outdoor mountaineering. Intelligent mountaineering equipment can provide some safe and convenient services for outdoor mountaineering. However, the positioning accuracy is affected by the ability of remote sensing image recognition in the background. To address the haze interference in remote sensing image positioning, the Retinex algorithm is enhanced using an atmospheric scattering model. This improved Retinex algorithm adopts a multiscale retinal enhancement algorithm with color restoration to enhance images. The Gaussian kernel function plays a filtering role, while the guided filtering is used to improve image texture and details. Test data from background remote sensing images of mountaineering equipment are used to evaluate the algorithm. Results indicate that when the entire test set is used for calculations, the normalized information entropy of I-Retinex, Retinex, generative adversarial networks, and Alex models is 0.92. The median values of normalized mean squared error and mean absolute error are 0.13 and 0.15, respectively, outperforming the contrast defogging model. There is a negative correlation between the normalized peak signal-to-noise ratio and haze noise error in each model. When the entire test set is used, the normalized average gradient of I-Retinex is 0.87, significantly higher than the comparison models. However, the I-Retinex model developed in this study lacks optimal average computation time and memory consumption data. Experimental results demonstrate that the improved haze removal model effectively removes haze from remote sensing images, supporting the remote sensing image-related service functionalities of mountaineering equipment.

An Intelligent Proofreading for Remote Skiing Actions Based on Variable Shape Basis

AbstractThe current proofreading algorithms for action regulation mainly recover the 3D structure and action information of non-rigid objects from image sequences by factorization. Most of algorithms assume that the camera model is an affine model. This assumption only holds if the size and depth of the object change very little relative to the distance from the object to the camera, which is in the case of fixed-shape basis. When the object is very close to the camera, this assumption causes a large reconstruction error. This paper solves this problem by the intelligent proofreading algorithms for remote skiing teaching actions based on variable shape basis. Firstly, the improved Retinex algorithm is used to enhance the multi-frame video images of skiing actions to make the action details more prominent. Then, measurement matrix is calculated after eliminating the translation vector by coordinate transformation. Under the condition of rank constraint, the measurement matrix is decomposed by singular value decomposition algorithm, and the correct shape basis structure of 3D action features can be obtained by using the variable shape basis. Finally, by randomly initializing a parameter, the optimized parameter and the least square algorithm are used to optimize the randomly initialized parameter further. The iteration until the convergence of the objective function can be used to calculate the deformation degree of the actions. The test results show that this algorithm improves the proofreading accuracy of action regulation in skiing teaching, and the proofreading results of various uploaded sliding actions are correct, which can be applied to remote skiing teaching and community learning.

The Retinex enhancement algorithm for low‐light intensity image based on improved illumination map

AbstractTaken in low‐light intensity conditions, image with low brightness affects processing precision. In this article, the Gamma Function based on the brightness average and weighted fusion method according to gray entropy is proposed, which is combined with the improved Retinex algorithm. First, the maximum values of R, G, and B channels in original image are extracted to generate the primary illumination map. Second, the illumination map is optimized and adjusted via the Gamma correction function based on the average brightness value. Finally, the illumination map and detail layer are fused by a weighted fusion algorithm of gray entropy to obtain the reflection map. Reflection maps are used as enhancement. The algorithm proposed in this article can improve the brightness and maintain light distribution in the original image with higher precision and less color distortion.

Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex

Aiming at solving the problems of local halo blurring, insufficient edge detail preservation, and serious noise in traditional image enhancement algorithms, an improved Retinex algorithm for low-light mine image enhancement is proposed. Firstly, in HSV color space, the hue component remains unmodified, and the improved multi-scale guided filtering and Retinex algorithm are combined to estimate the illumination and reflection components from the brightness component. Secondly, the illumination component is equalized using the Weber–Fechner law, and the contrast limited adaptive histogram equalization (CLAHE) is fused with the improved guided filtering for the brightness enhancement and denoising of reflection component. Then, the saturation component is adaptively stretched. Finally, it is converted back to RGB space to obtain the enhanced image. By comparing with single-scale Retinex (SSR) algorithm and multi-scale Retinex (MSR) algorithm, the mean, standard deviation, information entropy, average gradient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) are improved by an average of 50.55%, 19.32%, 3.08%, 28.34%, 29.10%, and 22.97%. The experimental dates demonstrate that the algorithm improves image brightness, prevents halo artifacts while retaining edge details, reduces the effect of noise, and provides some theoretical references for low-light image enhancement.

Improved Retinex algorithm for low illumination image enhancement in the chemical plant area

Due to the complexity of the chemical plant area at night and the harsh lighting environment, the images obtained by monitoring equipment have issues such as blurred details and insufficient contrast, which is not conducive to the subsequent target detection work. A low illumination image enhancement model based on an improved Retinex algorithm is proposed to address the above issues. The model consists of a decomposition network, an adjustment network, and a reconstruction network. In the decomposition network, a new decomposition network USD-Net is established based on U-Net, which decomposes the original image into illumination and reflection maps, enhancing the extraction of image details and low-frequency information; Using an adjustment network to enhance the decomposed lighting image, and introducing a Mobilenetv3 lightweight network and residual structure to simplify the network model and improve the contrast of the image; In the reconstruction network, the BM3D method is used for image denoising to enhance the ability to restore image detail features; The enhanced illumination and reflection images were fused based on the Retinex algorithm to achieve low illumination image enhancement in the chemical plant area. This article uses five image quality evaluation indicators, namely Peak Signal-to-Noise Ratio, Structural Similarity Index, Natural Image Quality Evaluator, Interpolation Error, and Level of Effort, to compare eight traditional or modern algorithms and evaluate three different types of datasets. The experimental results show that the improved algorithm enhances the texture details of the image, improves the contrast and saturation of the image, and has good stability and robustness, which can effectively meet the needs of low illumination image enhancement in chemical plant areas.

Research on Improved Retinex-Based Image Enhancement Method for Mine Monitoring

An improved Retinex fusion image enhancement algorithm is proposed for the traditional image denoising methods and problems of halo enlargement and image overexposure after image enhancement caused by the existing Retinex algorithm. First, a homomorphic filtering algorithm is used to enhance each RGB component of the underground coal mine surveillance image and convert the image from RGB space to HSV space. Second, bilateral filtering and multi-scale retinex with color restoration (MSRCR) fusion algorithms are used to enhance the luminance V component while keeping the hue H component unchanged. Third, adaptive nonlinear stretching transform is used for the saturation S-component. Last, the three elements are combined and converted back to RGB space. MATLAB simulation experiments verify the superiority of the improved algorithm. Based on the same dataset and experimental environment, the improved algorithm has a more uniform histogram distribution than the multi-scale Retinex (msr) algorithm and MSRCR algorithm through comparative experiments. At the same time, the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), standard deviation, average gradient, mean value, and colour picture information entropy of the images were improved by 8.28, 0.15, 4.39, 7.38, 52.92 and 2.04, respectively, compared to the MSR algorithm, and 3.97, 0.02, 34.33, 60.46, 26.21, and 1.33, respectively, compared to the MSRCR algorithm. The experimental results show that the image quality, brightness and contrast of the images enhanced by the improved Retinex algorithm are significantly enhanced, and the amount of information in the photos increases, the halo and overexposure in the images are considerably reduced, and the anti-distortion performance is also improved.

Underwater Image Enhancement Based on Adaptive Color Correction and Improved Retinex Algorithm

In order to solve the problems about color distortion and low contrast of underwater images, we propose an underwater image enhancement algorithm that combines adaptive color correction with improved Retinex algorithm. Our algorithm is a single-image enhancement method that does not require specialized hardware and underwater scenes prior. Firstly, the adaptive color correction is carried out on the underwater distorted images to solve the color cast problem effectively. Then, on the one hand, we use the image decomposition to strengthen the detail part and obtain a detail enhanced image. On the other hand, we use the improved Retinex algorithm to strengthen the edge part and obtain an edge enhanced image. Finally, the detail enhanced image and the edge enhanced image are fused based on the non-subsampled shearlet transform (NSST) to obtain the final enhanced underwater image. The results show that our method outperforms several state-of-the-art methods about underwater image enhancement in terms of PCQI, UCIQE, UIQM and IE. By scale invariant feature transform (SIFT) algorithm, we calculate the number of feature matching points of the input image and the enhanced image, and our proposed method achieves the best experimental results. The source code of our proposed algorithm is available at: https://github.com/lin9393/ underwater-image-enhance.

Retinex Algorithm and Mathematical Methods Based Texture Detail Enhancement Method for Panoramic Images

A panoramic image texture detail enhancement method based on the Retinex algorithm is proposed to work on the nature of panoramic images. Firstly, the panoramic image is collected, then the panoramic image is preprocessed through brightness enhancement, the brightness of the preprocessed panoramic image is normalized, and the panoramic image is optimized and increased by the improved Retinex algorithm. Finally, the simulation test of the panoramic image was carried out. The results show that the improved Retinex algorithm works on the sign to-commotion proportion of the panoramic image. Furthermore, the time required to enhance the panoramic texture detail is short, which can meet the practical requirements of panoramic image subsequent processing.

Research on low illumination coal gangue image enhancement based on improved Retinex algorithm

ABSTRACT Aiming at the problems of coal and gangue image recognition in low illumination or high dust concentration environment, an adaptive enhancement method based on improved Retinex algorithm, is proposed: H-GF-MSR algorithm. The algorithm is processed in HSV color space, uses guided filter for multi-scale Retinex algorithm in brightness component, and carries out adaptive saturation stretching in saturation component. Then the images are processed by histogram equalization, and the two algorithms are fused to improve the accuracy. Compared with SSR algorithm, MSRCR algorithm and other classical algorithms, this algorithm is improved in suppressing the generation of halo and image edge enhancement. The experimental results show that in terms of image evaluation indexes such as information entropy, image mean, image average gradient and peak signal-to-noise ratio, compared with SSR algorithm, the algorithm improves by 5.4%, 41.1%, 33.2% and 0.4% respectively; compared with MSRCR algorithm, the average improvement is 2.9%, 32.4%, 31.7% and 0.7% respectively. Through the enhancement experiments of coal gangue image in the illumination range of 10 lux ~ 200 lux, the algorithm has better enhancement effect. It can effectively suppress the bright light and restore the original details of the object.

Low illumination Image Enhancement based on Improved Retinex Algorithm

<p>Aiming at the problems of insufficient illumination and low contrast of low illumination image, an improved Retinex low illumination image enhancement algorithm is proposed. Firstly, the brightness component V of the original image is extracted in HSV color space, and its enhancement by Single-Scale Retinex (SSR) is used to obtain the reflection component. For the edge problem caused by the estimation of illumination component, the Gaussian weighted bilateral filter is used as the filter function to maintain the edge information. Then, the saturation component S is adaptively stretched to improve the color saturation. However, different low illumination images have different contrast, and some images have insufficient contrast enhancement, so a global adaptive algorithm is introduced to modify the contrast and obtain the final image. According to the logarithmic characteristics of human vision, it can adaptively enhance the contrast of different images without over enhancement. Experimental results show that the proposed algorithm can effectively improve the visual quality of the image, the contrast is improved significantly and image edge details are protected, and objective evaluations such as average gradient, information entropy and peak signal-to-noise ratio have been improved.</p> <p> </p>

Research on Dehazing Algorithm of Single UAV Reconnaissance Image under Different Landforms Based on Retinex

Aiming at the weakness of the dark channel prior modelin and Retinex algorithm such as slow calculation speed, halo effect, and loss of details in the processing of UAV single image dehazingand, an improved Retinex algorithm is proposed. First, convert the RGB color space to the HIS color space, separate the brightness I, assign values to the three scales of large, medium and small, use Gaussian surround to obtain the three scale brightness images and then weight, and then switch the brightness image to the RGB color space, in order to achieve the information with more details. Finally, the haze images which is distinguished in three different landforms such as city, mountainous area and forestwill be performed simulation experiments in MATLAB, and adjust the image restoration effect by changing the parameters.

GPU fast restoration of non-uniform illumination images

This paper presents a GPU based parallel implementation for the non-uniform illumination image restoration method, which uses a retinex based algorithm to decompose the original image into brightness and reflectance components, and adjusts the brightness value through an adaptive gamma correction and nonparametric mapping to achieve the restoration. Specifically, we parallelize the improved retinex algorithm on GPU to extract the brightness value of each pixel. After that, the probability of different brightness range is counted through each block to the entire image to reduce the competition of memory access. Finally, we use two different parallel reduce methods to calculate the probability density and cumulative density of brightness value and generate the mapping curve. The experiment conducted on three different GPUs and two CPUs with different resolution images shows that our method can process a 1024 × 2048 image in 1.024 ms on RTX2080Ti, indicates a great potential for real-time application.

An improved image registration and fusion algorithm

Under the background of telemedicine, a new registration and mosaic algorithm for medical images is proposed in this paper to solve the problems of electronic noise, uneven illumination and ray scattering in the real-time medical process. The improved Retinex algorithm by trilateral filter and homomorphic filtering algorithm can enhance the image effectively in preprocessing. The improved phase correlation algorithm based on log polar transformation was used to calculate parameters, such as rotation, scaling and translation. Then, the SUSAN corner matching points were extracted in overlapping positions, the improved KD tree was used for enhancing matching efficiency. Later, matching points were purified by the improved RANSAC algorithm. Finally, images were processed by Laplacian pyramid decomposition algorithm to make the image joint seemed smooth and natural. The results of experiments and evaluation criteria confirm that the new method has high robustness in the process of medical image registration and stitching in the network.

Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm

To address the problem of unclear images affected by occlusion from fog, we propose an improved Retinex image enhancement algorithm based on the Gaussian pyramid transformation. Our algorithm features bilateral filtering as a replacement for the Gaussian function used in the original Retinex algorithm. Operation of the technique is as follows. To begin, we deduced the mathematical model for an improved bilateral filtering function based on the spatial domain kernel function and the pixel difference parameter. The input RGB image was subsequently converted into the Hue Saturation Intensity (HSI) color space, where the reflection component of the intensity channel was extracted to obtain an image whose edges were retained and are not affected by changes in brightness. Following reconversion to the RGB color space, color images of this reflection component were obtained at different resolutions using Gaussian pyramid down-sampling. Each of these images was then processed using the improved Retinex algorithm to improve the contrast of the final image, which was reconstructed using the Laplace algorithm. Results from experiments show that the proposed algorithm can enhance image contrast effectively, and the color of the processed image is in line with what would be perceived by a human observer.

An Improved Algorithm Inspired by Natural Biology for Image Enhancement Used in Space Station

In the space station, the uneven illumination of image brings significant challenges for image enhancement in vision system due to high dynamic range of light intensity and the light reflection of metal facilities. In order to solve this problem in monitoring, unattended operation and robot operation, Retinex algorithm was proposed to estimate the luminosity. However, the effectiveness of this method is highly dependent on the selected path and consumes huge computational cost. This paper proposes an algorithm inspired by the Archimedes spiral in natural environment to improve the processing time and enhance the images quality. First, the structure and mechanism of Human Visual System (HVS) are analyzed. Second, the original image is decomposed into the brightness and color layers based on the structural human eyes mechanism. Third, the brightness layer is processed by the improved Retinex algorithm to enhance the original image by merging it with the color layer. The proposed algorithm is validated with the experimental results.

Foggy image enhancement technology based on improved Retinex algorithm

Foggy image enhancement technology based on improved Retinex algorithm

3D Image Enhancement Model for Large-scale Scene Based on Improved Retinex Algorithm

3D Image Enhancement Model for Large-scale Scene Based on Improved Retinex Algorithm

Illumination Processing Recognition of Face Images Based on Improved Retinex Algorithm

In order to change the defects of the similar traditional Retinex algorithm that it is easy to appear the phenomenon in the illumination image acquisition process, we proposed a study on the face images recognition under the illumination condition based on the improved Retinex Algorithm, which is based on the Retinex Theory Algorithm defects and analyzes the brightness, contrast rate of the face images as well as the design of the illumination background in the improved Retinex, and draw the conclusion that this algorithm can overcome the Halo phenomenon to a certain extent when occurs the illumination mutation. In order to verify the effectiveness of the algorithm, we did a simulation to the face images in the Yale B face database finally and studied the comparison test of similar algorithms in different images. The result shows that the improved Retinex algorithm can be a good solution to the Halo problem, reduce the time complexity and can be applied to the face recognition under the condition of strong illumination changes, the recognition rate of which is higher, and the illumination robustness is stronger.

An Improved Algorithm Based on Retinex Theory for X-Ray Medical Image

Retinex theory combined the elements of images and visual.This paper improved the Retinex-based medical image enhancement method, It can get better brightness by using the neural network logarithmic The S-shaped LogSig transfer function instead of the original MSR logarithm function. Based on this, the paper presents a composite LRA (LogSig Retinex Algorithm) algorithm, and analysed the shortcomings of the original Retinex algorithm applied to the X-ray medical image analysis, described the advantage of the composite LRA algorithm is better than traditional Retinex algorithm on the X-ray medical image. Experimental results show that the improved Retinex algorithm can achieve not only low-contrast medical image enhancement, but also the dynamic range compression of the image, can significantly improve the information of the medical image of the dark area. It has practical significance for clinical diagnosis.

Improved Retinex Image Enhancement Algorithm

Abstract Retinex method mainly consists of two steps: estimation and normalization of illumination. How to extract the background illumination accurately is a key problem. The backgrounds of picture sequence in video's adjacent frames are usually similar and closely related. More accurate illumination information can be extracted when this characteristics of video's picture sequence is considered. In the paper, we propose an improved Retinex algorithm. Filter the images using the Gauss masks of different scale and parameter for each frame image, and all these filtering results are fused together by minimum method. In the paper, the scale of Gauss filters are set as 5, 9, 13, 25, and their variance set as 0.3, 0.5, 0.7 and 1.0 respectively. 6 adjacent frame images are selected, and the uniform and optical background image for these 6 images can be extracted by maximum method. This method makes use of the similarity and relationship among the adjacent frame images in videos. Enhance the images using Retinex method with this optical background image as their uniform illumination information. Experiment shows that more accurate back grounds are acquired and more excellent enhancement performance are achieved.