Image enhancement with intensity transformation on embedding space

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained attention.However,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality images.In order to address this limitation,a simple yet effective approach for image enhancement is proposed.The proposed algorithm based on the channel-wise intensity transformation is designed.However,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to colours.To this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding space.Then,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced images.Extensive experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space metrics.Specifically,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch similarity.Also,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

Pixel’s Quantum Image Enhancement Using Quantum Calculus

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values.The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone.The brain Magnetic Resonance Imaging(MRI)scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer.Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease.To solve this issue,this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis.The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability.The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety ofMRIscan datasets of variable quality.ForMRI scans,the BRISQUE“blind/referenceless image spatial quality evaluator”and the NIQE“natural image quality evaluator”measures were 39.38 and 3.58,respectively.The proposed image enhancement model,according to the data,produces the best image quality ratings,and it may be able to aid medical experts in the diagnosis process.The experimental results were achieved using a publicly available collection of MRI scans.

A Text Image Watermarking Algorithm Based on Image Enhancement

Digital watermarking technology is adequate for copyright protection and content authentication.There needs to be more research on the watermarking algorithm after printing and scanning.Aiming at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark,an anti-print scanning watermarking algorithm suitable for text images is proposed.This algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform(DWT)-Singular Value Decomposition(SVD)to embed the watermark.Experiments show that the average Normalized Correlation(NC)of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group(JPEG)compression,JPEG2000 compression,and print scanning is above 0.93.Especially,the average NC of the watermark extracted after print scanning attacks is greater than 0.964,and the average Bit Error Ratio(BER)is 5.15%.This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Low-light image enhancement methods have limitations in addressing issues such as color distortion,lack of vibrancy,and uneven light distribution and often require paired training data.To address these issues,we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network(RFNet),which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms.This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images.In the first stage,we design a multi-scale feature extraction module based on Retinex theory,capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images.In the second stage,an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features,and the generated images are fused with the original input images to complete the low-light image enhancement.Experiments show the effectiveness and rationality of each module designed in this paper.And the method reconstructs the details of contrast and color distribution,outperforms the current state-of-the-art methods in both qualitative and quantitative metrics,and shows excellent performance in the real world.

Removal of Stripes in Remote Sensing Images Based on Statistics Combined with Image Enhancement

A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method.The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline.Due to the differences in satellite sensors when producing images,subtle but inherent stripes can appear at the stitching positions between the sensors.These stitchingstripes cannot be eliminated by conventional relative radiometric calibration.The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation,classification and interpretation of remote sensing images.Therefore,a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper.First,the inconsistency in grayscales around stripes is eliminated with the statistical method.Second,the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality.Finally,the details of the images are highlighted by a new image enhancement method,which makes the whole image clearer.Comprehensive experiments are performed,and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.

Underwater Image Enhancement Based on IMSRCR and CLAHE-WGIF

Aiming at the scattering and absorption of light in the water body,which causes the problems of color shift,uneven brightness,poor sharpness and missing details in the acquired underwater images,an underwater image enhancement algorithm based on IMSRCR and CLAHE-WGIF is proposed.Firstly,the IMSRCR algorithm proposed in this paper is used to process the original underwater image with adaptive color shift correction;secondly,the image is converted to HSV color space,and the segmentation exponential algorithm is used to process the S component to enhance the image saturation;finally,multi-scale Retinex is used to decompose the V component image into detail layer and base layer,and adaptive two-dimensional gamma correction is made to the base layer to adjust the brightness unevenness,while the detail layer is processed by CLAHE-WGIF algorithm to enhance the image contrast and detail information.The experimental results show that our algorithm has some advantages over existing algorithms in both subjective and objective evaluations,and the information entropy of the image is improved by 6.3%on average,and the UIQM and UCIQE indexes are improved by 12.9%and 20.3%on average.

Research and Implementation of Algorithm for Image Enhancement and Unwrapped Distortion Correction for SLVF Panoramic Night Vision Image

Abstract： Based on digital signal processor（DSP） and field programmable gate array（FPGA） techniques, the architecture of super large view field（SLVF） panoramic night vision image processing hardware platform was established. The panoramic unwrapping and correcting algorithm, up to a full 360°, based on coordinate rotation digital computer （CORDIC） and night vision image enhancement algorithm, based on histogram equalization theory and edge detection theory, was presented in this paper, with the purpose of processing night vision dynamic panoramic annular image. The annular image can be unwrapped and corrected to conventional rectangular panorama by the panoramic image processing algorithm, which uses the pipelined CORDIC configuration to realize a trigonometric function generator with high speed and high precision. Histogram equalization algorithm can perfectly enhance the contrast of the night vision image. Edge detection algorithm can be propitious to find and detect small dim dynamic targets in night vision circumstances. After abundant experiment, the al- gorithm for panoramic image processing and night vision image enhancement is successfully implemented in FPGA and DSP. The panoramic night vision image system is a compact device, with no external rotating parts. And the system can reliably and dynamically detect 360＊ SLVF panoramic night vision image.

Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

Interactive Image Enhancement by Fuzzy Relaxation

In this paper, an interactive image enhancement （HE） technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications. First, based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement, an improved version of this algorithm, which is called FuzzIIE Method 1, is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm. Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function, in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.

Single Image Enhancement in Sandstorm Weather via Tensor Least Square

In this paper,we present a tensor least square based model for sand/sandstorm removal in images.The main contributions of this paper are as follows.First,an important intrinsic natural feature of outdoor scenes free of sand/sandstorm is found that the outlines in RGB channels are somewise similar,which discloses the physical validation using the tensor instead of the matrix.Second,a tensor least square optimization model is presented for the decomposition of edge-preserving base layers and details.This model not only decomposes the color image(taken as an inseparable indivisibility)in X,Y directions,but also in Z direction,which meets the statistical feature of natural scenes and can physically disclose the intrinsic color information.The model’s advantages are twofold:one is the decomposition of edgepreserving base layers and details that can be employed for contrast enhancement without artificial halos,and the other one is the color driving ability that makes the enhanced images as close to natural images as possible via the inherent color structure.Thirdly,the tensor least square optimization model based image enhancement scheme is discussed for the sandstorm weather images.Finally,the experiments and comparisons with the stateof-the-art methods on real degraded images under sandstorm weather are shown to verify our method’s efficiency.

New algorithm for infrared small target image enhancement based on wavelet transform and human visual properties

The key to the wavelet based denoising teehniquea is how to manipulate the wavelet coefficients. By referring to the idea of Inclusive-OR in the design of circuits, this paper proposes a new algorithm called wavelet domain Inclusive-OR denoising algorithm（WDIDA）, which distinguishes the wavelet coefficients belonging to image or noise by considering their phases and modulus maxima simultaneously. Using this new algorithm, the denoising effects are improved and the computation time is reduced. Furthermore, in order to enhance the edges of the image but not magnify noise, a contrast nonlinear enhancing algorithm is presented according to human visual properties. Compared with traditional enhancing algorithms, the algorithm that we proposed has a better noise reducing performanee , preserving edges and improving the visual quality of images.

A comprehensive review of medical image enhancement technologies

Image enhancement plays an important role in many applications of medical imaging. Image enhancement technologies can improve the qualities of medical images with low contrast and high level noise by stretching contrast, suppressing noise and so on. Such images processed by image enhancement technologies are helpful to doctors in analyses and diagnoses. In this paper, we present a technical review of various existing image enhancement methodologies which are often emoloved.

A New Medical Image Enhancement Algorithm Based on Fractional Calculus

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images.The captured images may present with low contrast and low visibility,which might inuence the accuracy of the diagnosis process.To overcome this problem,this paper presents a new fractional integral entropy(FITE)that estimates the unforeseeable probabilities of image pixels,posing as the main contribution of the paper.The proposed model dynamically enhances the image based on the image contents.The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’probability.Initially,the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image.Next,the contrast of the image is then adjusted to enhance the regions with low visibility.Finally,the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image.Tests were conducted on brain MRI,lungs CT,and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality.The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores.Overall,this model improves the details of brain MRI,lungs CT,and kidney MRI scans,and could therefore potentially help the medical staff during the diagnosis process.

Development of a Compact Photoacoustic Tomography Imaging System with Dual Single-Element Transducers for Image Enhancement

Objective:This paper proposes a new photoacoustic computed tomography(PACT)imaging system employing dual ultrasonic transducers with different frequencies.When imaging complex biological tissues,photoacoustic(PA)signals with multiple frequencies are produced simultaneously;however,due to the limited bandwidth of a single-frequency transducer,the received PA signals with specific frequencies may be missing,leading to a low imaging quality.Methods:In contrast to our previous work,the proposed system has a compact volume as well as specific selection of the detection center frequency of the transducer,which can provide a comprehensive range for the detection of PA signals.In this study,a series of numerical simulation and phantom experiments were performed to validate the efficacy of the developed PACT system.Results:The images generated by our system combined the advantages of both high resolution and ideal brightness/contrast.Conclusion:The interchangeability of transducers with different frequencies provides potential for clinical deployment under the circumstance where a single frequency transducer cannot perform well.

A Novel Nonlinear Algorithm for Typhoon Cloud Image Enhancement

A novel nonlinear gray transform method is proposed to enhance the contrast of a typhoon cloud image.Generally,the typhoon cloud image obtained by a satellite cannot be directly used to make an accurate prediction of the typhoon＇s center or intensity because the contrast of the received typhoon cloud image may be bad.Our aim is to extrude the typhoon＇s eye in the typhoon cloud image.A normalized arc-tangent transformation operation is designed to enhance global contrast of the typhoon cloud image.Differential evolution algorithm is used to choose the optimal nonlinear transform parameter.Finally,geodesic activity contour model is used to extract the typhoon＇s eye to verify the performance of the proposed method.Experimental results show that the proposed method can efficiently enhance the global contrast of the typhoon cloud image while greatly extruding the typhoon＇s eye.

Image enhancement of color fundus photographs for age-related macular degeneration:the Shanghai Changfeng Study

AIM:To develop and evaluate a new fundus image optimization software based on red,green,blue channels(RGB) for the evaluation of age-related macular degeneration(AMD) in the Chinese population.METHODS:Fundus images that were diagnosed as AMD from the Shanghai Changfeng Study database were analyzed to develop a standardized optimization procedure.Image brightness,contrast,and color balance were measured.Differences between central lesion area and normal retinal area under different image brightness,contrast,and color balance were observed.The optimal optimization parameters were determined based on the visual system to avoid image distortion.A paired-sample diagnostic test was used to evaluate the enhancement software.Fundus optical coherence tomography(OCT) was used as the gold standard.Diagnostic performances were compared between original images and optimized images using Mc Nemar’s test.RESULTS:A fundus image optimization procedure was developed using 86 fundus images of 74 subjects diagnosed with AMD.By observing gray-scale images,choroid can be best displayed in red channel and retina in green channel was found.There was limited information in blue channel.Totally 104 participants were included in the paired sample diagnostic test to assess the performance of the optimization software.After the image enhancement,sensitivity increased from 74% to 88%(P=0.008),specificity decreased slightly from 88% to 84%(P=0.500),and Youden index increased by 0.11.CONCLUSION:The standardized image optimization software increases diagnostic sensitivity and may help ophthalmologists in AMD diagnosis and screening.

Underwater Diver Image Enhancement via Dual-Guided Filtering

The scattering and absorption of light propagating underwater cause the underwater images to present lowcontrast,color deviation,and loss of details,which in turn make human posture recognition challenging.To address these issues,this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method.First,the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image.Second,dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details.Four feature weight maps of the two images were then calculated,and two normalizedweightmapswere constructed formulti-scale fusion using normalization.To better preserve the obtained image details,the fusion image was histogram-stretched to obtain the final enhanced result.The experimental results validated that this method has improved the accuracy of underwater human posture recognition.