Multi-scale Retinex Research Articles

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.

An underwater target recognition method based on improved YOLOv4 in complex marine environment

In the marine environment, there are problems such as complex background and low illumination, resulting in poor picture quality, and the aggregation of small targets and multiple targets brings difficulties to target recognition. In order to improve the accuracy of marine target detection, the image enhancement and improved YOLOv4 algorithm are used to identify marine organisms. Firstly, aiming at the problems of image blur, some of the images are enhanced with the multi-scale retinex with colour restoration (MSRCR) enhancement algorithm so that the image is clearer and it is easier to extract features. Secondly, Mosaic augmentation is added to YOLOv4 to enrich the data set and increase network robustness. Then the Spatial Pyramid Pooling (SPP) module of YOLOv4 is improved by changing the size of the pooling core, which increases the range of feature extraction and improves the detection capabilities, and its mAP value reaches 97.06%. The experimental results show that the detection accuracy of the image enhancement and improved YOLOv4 algorithm is 7.16% higher than that of the original algorithm. And the improved YOLOv4 algorithm is improved in average precision and recall rate compared with other algorithms, which verifies the effectiveness of the algorithm.

Fractional-order Differintegral based multiscale Retinex inspired texture dependent quality enhancement for remotely sensed images

Fractional-order Differintegral based multiscale Retinex inspired texture dependent quality enhancement for remotely sensed images

Soybean image segmentation based on multi-scale Retinex with color restoration

Aiming at the matter of seed overlapping and adhesion within the actual quality detection of soybean, a soybean image segmentation based on multi-scale Retinex with color restoration (MSRCR) was proposed. Firstly, original images of soybean were obtained by the industrial camera. secondly, these images were enhanced by MSRCR. Thirdly, these images after MSRCR were segmented by Otsu. Finally, soybean seed images were extracted by mask and minimum bounding rectangle from soybean images after MSRCR. The image acquisition system was constructed and the algorithm was verified. The experiment showed that the segmentation accuracy of the proposed algorithm was 98.05%, which can meet the requirement of soybean image segmentation. Additionally, the proposed algorithm can also provide an image preprocessing method for agricultural seed classification in similar scenes.

Vehicle detection in foggy weather based on an enhanced YOLO method

Vehicle detection is the key to driverless technology. For safety, driverless technology requires extremely high accuracy and real-time for vehicle detection in different situations. In this paper, we study an enhanced YOLO -based algorithm for vehicle detection in foggy weather conditions. We add a dehazing module in the YOLO model for more information restoration, which is built by the multi-scale retinex with color restoration (MSRCR). And the enhanced model is trained with the augmentation data processed MSRCR for more stable performance. We evaluate our method in the public dataset, the results show the enhanced YOLO model has better performance than conventional YOLO in vehicle detection in foggy weather.

Mask Wearing Detection Based on Inceptionv4 and Multi-Scale Retinex Image Enhancement Algorithm

The novel coronavirus is a contagious virus with a high mortality rate, and the international health emergency due to COVID-19 has never stopped since the outbreak, due to this situation, wearing masks has become a basic recommended public epidemic prevention approach for many countries. In the current shortage of medical human resources, we urgently need a non-artificial mask-wearing detection method. In this paper, the multi-scale Retinex algorithm is involved as the preprocessing step of the input image. The mask-wearing detection is based on the InceptionV4 convolutional neural network model. During the experiment, we compared and verified the superiority of the Inception part of the InceptionV4 model compared with the Stem structure of the GoogLenet model, and from LFW (Labeled Faces in the Wild), RMFD (Real-World-Masked-Face-Dataset) more than 10,000 samples were selected from the public datasets for model training. Finally, the precise rate reaches 97.3 %.

Computer Graphic Image Design and Visual Communication Design in the Internet of Things Scenario

For the problems of low-illumination reconstructed images with low resolution and long reconstruction time, an image reconstruction system based on the wavelet domain chunking compressed perception algorithm is proposed. A low-illumination image sampling model is established, and the wavelet domain chunking compression perception and information fusion processing are performed using the depth-of-field adaptive adjustment method of the image. The multiscale Retinex algorithm is used for wavelet domain chunking compressed perception and information extraction, and the information entropy feature quantity of the image is extracted. The IoT-based artificial intelligence image detection system has been formed, which effectively enhances the accuracy and timeliness of the image detection system and makes up for the deficiencies of the traditional image detection system. Simulation results show that the method is used for low-illumination image reconstruction with higher resolution, better edge perception, and shorter reconstruction time, which is more efficient for practical application.

Study on steel plate scratch detection based on improved MSR and phase consistency

In the casting process of the steel plate, due to the influence of rolling equipment and technology, the defects such as cracks and scratches appear on the surface of steel plate, which affect the performance of steel plate and even cause production accidents. In this paper, an automatic detection method for steel plate scratch is proposed. Firstly, the steel plate image is decomposed by channel and the enhanced image is obtained by the improved MSR (Multi-Scale Retinex) enhancement algorithm. Then, the phase consistency is detected after the Log Gabor wavelet transform and the scratch areas are obtained by the threshold segmentation and intersection of three channels. Finally, the scratch position is identified and the scratch characteristics such as width and length can be calculated. The results show that the minimum error of the characteristics measurement is only 2.28% in the experimental environment and 4.15% in the field environment, and the mean running time is 0.2826 s in the experimental environment and 0.3193 s in the field environment. It verifies that the proposed method is effective and practical.

Evaluation of underwater image enhancement algorithms based on Retinex and its implementation on embedded systems

The improvement of underwater imaging has advanced significantly due to its contribution to marine engineering and underwater exploration. This fact has been reflected in recent years with the proposal of numerous algorithms that improve the quality of underwater images. A benchmarking of three algorithms based on the Retinex models implemented on five high-performance embedded systems is presented herein. These algorithms are the Single Scale Retinex Model (SSR), Multi-Scale Retinex Model (MSR), and the Multi-Scale Retinex Model with Color Restoration (MSRCR). These algorithms perform the histogram equalization to distribute pixels, reduce the predominant color, perform color and contrast correction, and achieve an automatic white balance to improve illumination. This paper employs five edge devices such as Beagle Board, Odroid-XU4, Raspberry Pi 4, Jetson Nano, and Jetson TX2 to enhance underwater images and benchmark their performance. Four quality metrics without a reference image such as UIQM, UCIQUE, BRISQUE and Entropy are used to evaluate the quality of the enhanced underwater images. The MSRCR algorithm achieves the best quality results when it is implemented on Jetson TX2 embedded system. It has a difference of 0.46 s in the processing time of 147 × 196 pixels images concerning a high-performance personal computer (PC). Implementing these algorithms on embedded systems offers an excellent cost-benefit ratio versus a traditional PC, considering image quality metrics, precision, accuracy, energy consumption, price, lightweight, size, portability, and reliability. These findings hold great promise for unmanned and self-propelled underwater vehicles with artificial vision for exploration.

Research on Underwater Target Recognition Technology Based on Neural Network

At present, the underwater environment required by the seafood aquaculture industry is very bad, and the fishing operation is completed artificially. In this environment, the use of machine fishing instead of artificial fishing is the development trend in the future. By comparing the characteristics of different algorithms, the multiscale Retinex algorithm (autoMSRCR) is selected to deal with image color skew, blur, atomization, and other problems. Labelimg software is used to annotate underwater targets in the image and make data sets. Of these, 20% are used as test sets, 70% as training sets, and 10% as verification sets. The target detection network of You Only Look Once Version4 (YOLOv4) based on convolutional neural networks (CNN) is adopted in this paper. The main feature extraction network adopts CSPDarknet53 structure, and the feature fusion network adopts SSP, and PANet network carries out sampling and convolution operations. The prediction output of extracted features is carried out through YoloHead network. After training the recognition model of the training sets, the detection effect is obtained by testing the data of the test sets. The identification accuracy of sea cucumber and sea urchin is 90.8% and 87.76%, respectively. Experiments show that the target detection network model can accurately identify the specified underwater organisms in the underwater environment.

Computer-Aided Image Enhanced Endoscopy Automated System to Boost Polyp and Adenoma Detection Accuracy.

Colonoscopy is the gold standard to detect colon polyps prematurely. Early detection, characterization and resection of polyps decrease colon cancer incidence. Colon polyp missing rate remains high despite novel methods development. Narrowed-band imaging (NBI) is one of the image enhance techniques used to boost polyp detection and characterization, which uses special filters to enhance the contrast of the mucosa surface and vascular pattern of the polyp. However, the single-button-activated system is not convenient for a full-time colonoscopy operation. We selected three methods to simulate the NBI system: Color Transfer with Mean Shift (CTMS), Multi-scale Retinex with Color Restoration (MSRCR), and Gamma and Sigmoid Conversions (GSC). The results show that the classification accuracy using the original images is the lowest. All color transfer methods outperform the original images approach. Our results verified that the color transfer has a positive impact on the polyp identification and classification task. Combined analysis results of the mAP and the accuracy show an excellent performance of the MSRCR method.

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization.

In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.

Presentation attack detection based on two-stream vision transformers with self-attention fusion

Aiming at the performance degradation of the existing presentation attack detection methods due to the illumination variation, a two-stream vision transformers framework (TSViT) based on transfer learning in two complementary spaces is proposed in this paper. The face images of RGB color space and multi-scale retinex with color restoration (MSRCR) space are fed to TSViT to learn the distinguishing features of presentation attack detection. To effectively fuse features from two sources (RGB color space images and MSRCR images), a feature fusion method based on self-attention is built, which can effectively capture the complementarity of two features. Experiments and analysis on Oulu-NPU, CASIA-MFSD, and Replay-Attack databases show that it outperforms most existing methods in intra-database testing and achieves good generalization performance in cross-database testing.

A novel haze removal computing architecture for remote sensing images using multi-scale Retinex technique

A novel haze removal computing architecture for remote sensing images using multi-scale Retinex technique

Underwater image enhancement by color correction and color constancy via Retinex for detail preserving

In underwater, light attenuation causes non-uniform illumination that degrades underwater image. To enhance the degraded image, we propose an underwater image enhancement method that includes color correction, color constancy, multi-scale retinex (MSR), edge-aware and texture smoothing filters, and contrast stretching. The formulated color correction compensates the red and blue channels by masking. Color constancy transforms non-uniform illumination into uniform illumination. Furthermore, the gamma correction in the color constancy improves the contrast of the image. The distorted colors are restored using the MSR with contrast limited adaptive histogram equalization (CLAHE). The edges are preserved using a fast local laplacian filter (FLLF), while the detailed texture of the image is smoothed using a rolling guidance filter (RGF) The contrast stretching enhances the image by stretching the pixel values. The qualitative and quantitative evaluation of the proposed algorithm outperforms the existing methods in terms of naturalness, contrast, visibility and enhancement of low-light images.

The Use of Scientific Programming Method under Visual Communication Design Concept in Urban Landscape Design

The purpose of this study is to provide more orderly and convenient urban planning ideas for the fast-paced life in the complex urban environment. The current sign design is analyzed according to the needs of urban residents for barrier-free sign design, and the sign design based on urban space color is established. An urban landscape visual communication optimization method is proposed based on hue saturation value (HSV) technology. The multiscale retinex (MSR) algorithm is used as a reference for simulation experiments. The experimental results show that the designed optimization method is significantly better than the traditional method in the expression effect of visual communication. First, the attention time of the sign design can be reduced by more than 3 seconds, which can effectively improve the lives of urban residents and tourists and improve their browsing efficiency. Next, 94% of the citizens believe that optimized urban signs are more prominent than traditional ones. Finally, the sign design optimization method proposed provides an image with a higher definition than the traditional sign design method. The proposed sign design and optimization scheme can effectively coordinate the relationship among urban landscape design, guided objects, and cities, help busy urban life, and provide new ideas for the development direction of visual communication design.

Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation.

The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the Particle Swarm Optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods.

Study on Personnel Detection Based on Retinex and YOLOv4 in Building Fire

When a fire occurs in a building, the internal environment is full of dense smoke, which will greatly hinder the evacuation and rescue of the trapped persons. If the evacuation and rescue are not in time, the life safety of the trapped persons will be seriously threatened. In response to this problem, this paper proposes a method for quickly detecting trapped persons in building fires. This method uses a combination of multi-scale Retinex image sharpening algorithm and YOLOv4 person detection algorithm. First obtain the image information of the fire scene, use the multi-scale Retinex algorithm based on the Gaussian pyramid to perform the sharpening process, and then use the YOLOv4 model to perform the personnel detection on the sharpened fire scene image. The experimental results show that the confidence of image person detection after Retinex sharpening processing has been significantly improved.

A Neural Network-Based Algorithm for Weak Signal Enhancement in Low Illumination Images

There is noise interference in low-illumination images, which makes it difficult to extract weak signals. For this reason, this paper proposes a low-illumination image weak signal enhancement algorithm based on neural network. Multi-scale normalization is performed on low-light images, and multi-scale Retinex is used to enhance weak signals in low-light images. On this basis, the GAN artificial neural network is used to detect the weak signal of the weak signal in the image, the normalization of the weak signal of the low-illumination image is completed based on the residual network, the self-encoding parameters of the depth residual are generated, and the weak signal enhancement result of the low-illumination image is output. The experimental results show that the method in this paper has better enhancement effect on low-illumination images and better image denoising effect. When the scale value is large, the low-contrast area of the low-illumination image has a better enhancement effect. The saturated area of the low-light image has a better enhancement effect.

Dark and Bright Channel Priors for Haze Removal in Day and Night Images

Removal of noise from images is very important as a clear, denoised image is essential for any application. In this article, a modified haze removal algorithm is developed by applying combined dark channel prior and multi-scale retinex theory. The combined dark channel prior (DCP) and bright channel prior (BCP) together with the multi-scale retinex (MSR) algorithm is used to dynamically optimize the transmission map and thereby improve visibility. The proposed algorithm performs effective denoising of images considering the properties of retinex theory. The proposed method removes haze on an image scene through estimation of the atmospheric light and manipulating the transmittance parameter. The coarse transmittance is refined using edge-preserving median filtering. Experimental results depict that the proposed technique enhances the image quality degraded by foggy weather during day time or night. The results show that the proposed algorithm enhances the image quality in terms of PSNR and SSIM to a considerate level.