Detecting Double JPEG Compressed Color Images via an Improved Approach

Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.

Mixed noise removal for color images using quaternion representation

In order to effectively restore color noisy images with the mixture of Gaussian noise and impulse noise,a new algorithm is proposed using the quaternion-based holistic processing idea for color images.First,a color image is represented by a pure quaternion matrix.Secondly,according to the different characteristics of the Gaussian noise and the impulse noise,an algorithm based on quaternion directional vector order statistics is used to detect the impulse noise. Finally,the quaternion optimal weights non-local means filter （QOWNLMF）for Gaussian noise removal is improved for the mixed noise removal.The detected impulse noise pixels are not considered in the calculation of weights.Experimental results on five standard images demonstrate that the proposed algorithm performs better than the commonly used robust outlyingness ratio-nonlocal means （ROR-NLM）algorithm and the optimal weights mixed filter （OWMF）.

Feature fusion method for edge detection of color images

A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.

Median Filtering Forensics Scheme for Color Images Based on Quaternion Magnitude-Phase CNN

In the paper,a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images.Compared with conventional convolutional neural network,color images can be processed in a holistic manner in the proposed scheme,which makes full use of the correlation between RGB channels.And due to the use of convolutional neural network,it can effectively avoid the one-sidedness of artificial features.Experimental results have shown the scheme’s improvement over the state-of-the-art scheme on the accuracy of color image median filtering detection.

Encryption and Decryption of Color Images through Random Disruption of Rows and Columns

In modern society,information is becoming increasingly interconnected through networks,and the rapid development of information technology has caused people to pay more attention to the encryption and the protection of information.Image encryption technology is a key technology for ensuring the security performance of images.We extracted single channel RGB component images from a color image using MATLAB programs,encrypted and decrypted the color images by randomly disrupting rows,columns and regions of the image.Combined with histograms and the visual judgments of encryption images,it is shown that the information of the original image cannot be obtained from the encryption image easily.The results show that the color-image encryptions with the algorithm we used have good effect and fast operation speed.Thus this algorithm has certain practical value.

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

A color image encryption scheme based on a 2D coupled chaotic system and diagonal scrambling algorithm

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.

Topic highlight on texture and color enhancement imaging in gastrointestinal diseases

Olympus Corporation developed texture and color enhancement imaging(TXI)as a novel image-enhancing endoscopic technique.This topic highlights a series of hot-topic articles that investigated the efficacy of TXI for gastrointestinal disease identification in the clinical setting.A randomized controlled trial demonstrated improvements in the colorectal adenoma detection rate(ADR)and the mean number of adenomas per procedure(MAP)of TXI compared with those of white-light imaging(WLI)observation(58.7%vs 42.7%,adjusted relative risk 1.35,95%CI:1.17-1.56;1.36 vs 0.89,adjusted incident risk ratio 1.48,95%CI:1.22-1.80,respectively).A cross-over study also showed that the colorectal MAP and ADR in TXI were higher than those in WLI(1.5 vs 1.0,adjusted odds ratio 1.4,95%CI:1.2-1.6;58.2%vs 46.8%,1.5,1.0-2.3,respectively).A randomized controlled trial demonstrated non-inferiority of TXI to narrow-band imaging in the colorectal mean number of adenomas and sessile serrated lesions per procedure(0.29 vs 0.30,difference for non-inferiority-0.01,95%CI:-0.10 to 0.08).A cohort study found that scoring for ulcerative colitis severity using TXI could predict relapse of ulcerative colitis.A cross-sectional study found that TXI improved the gastric cancer detection rate compared to WLI(0.71%vs 0.29%).A cross-sectional study revealed that the sensitivity and accuracy for active Helicobacter pylori gastritis in TXI were higher than those of WLI(69.2%vs 52.5%and 85.3%vs 78.7%,res-pectively).In conclusion,TXI can improve gastrointestinal lesion detection and qualitative diagnosis.Therefore,further studies on the efficacy of TXI in clinical practice are required.

Symbolic Images of Colors, Animals and Machines in The Red Badge of Courage

Abstract:Stephen Crane was an outstanding American novelist,poet,and journalist.He achieved great success in his literary works during his brief career.Crane’s most well-known work,The Red Badge of Courage,is commonly believed to be the first great novel of the American Civil War,largely because of its vivid and detailed description of the experience of warfare.This paper analyzes the images of color,animal and machine,which convey Crane’s thoughts of war:war is full of chaos,brutality,and confusion,without any romantic elements or heroism.

Studying pressure sores through illuminant invariant assessment of digital color images

Methods for pressure sore monitoring remain both a clinical and research challenge.Improved methodologies could assist physicians in developing prompt and effective pressure sore interventions.In this paper a technique is introduced for the assessment of pressure sores in guinea pigs,using captured color images.Sores were artificially induced,utilizing a system particularly developed for this purpose.Digital images were obtained from the suspicious region in days 3 and 7 post-pressure sore generation.Different segments of the color images were divided and labeled into three classes,based on their severity status.For quantitative analysis,a color based texture model,which is invariant against monotonic changes in illumination,is proposed.The texture model has been developed based on the local binary pattern operator.Tissue segments were classified,using the texture model and its features as inputs to a combination of neural networks.Our method is capable of discriminating tissue segments in different stages of pressure sore generation,and therefore can be a feasible tool for the early assessment of pressure sores.

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.

Quality measure of color images based on properties ofvision psychology and color distortion in MPEG-21

In this article, a new way to evaluate the quality of color images is proposed, in which the properties of human vision psychology, objective fidelity, edge information, and color distortion will be combined through utilizing 3-D matrix transform. There exists color redundancy and structural similarity between three different frames of a color image, the definition of vision properties will be measured by 3-D submatrix integration transform （SIT）, in which three color components are integrated into one model and color redundancy can be exploited fully. The simulation results show that the measure index is very effective and objective in accord with vision properties.

Segment-based coding of color images

Based on the idea of second generation image coding, a novel scheme for coding still images is pre- sented.At first, an image was partitioned with a pulse-coupled neural network; and then an improved chain code and the 2D discrete cosine transform was adopted to encode the shape and the color of its edges respectively.To code its smooth and texture regions, an improved zero-trees strategy based on the 2nd generation wavelet was chosen.After that, the zero-tree chart was selected to rearrange quantified coefficients.And finally some regulations were given according to psychology of various users.Experiments under noiseless channels demonstrate that the proposed method performs better than those of the current one, such as JPEG, CMP, EZW and JPEG2000.

Real-time ore sorting using color and texture analysis

Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.

Improved visibility of colorectal tumor by texture and color enhancement imaging with indigo carmine

BACKGROUND Accurate diagnosis and early resection of colorectal polyps are important to prevent the occurrence of colorectal cancer.However,technical factors and morphological factors of polyps itself can lead to missed diagnoses.Imageenhanced endoscopy and chromoendoscopy(CE)have been developed to facilitate an accurate diagnosis.There have been no reports on visibility using a combination of texture and color enhancement imaging(TXI)and CE for colorectal tumors.AIM To investigate the visibility of margins and surfaces with the combination of TXI and CE for colorectal lesions.METHODS This retrospective study included patients who underwent lower gastrointestinal endoscopy at the Toyoshima Endoscopy Clinic.We extracted polyps that were resected and diagnosed as adenomas or serrated polyps(hyperplastic polyps and sessile serrated lesions)from our endoscopic database.An expert endoscopist performed the lower gastrointestinal endoscopies and observed the lesion using white light imaging(WLI),TXI,CE,and TXI+CE modalities.Indigo carmine dye was used for CE.Three expert endoscopists rated the visibility of the margin and surface patterns in four ranks,from 1 to 4.The primary outcomes were the average visibility scores for the margin and surface patterns based on the WLI,TXI,CE,and TXI+CE observations.Visibility scores between the four modalities were compared by the Kruskal-Wallis and Dunn tests.RESULTS A total of 48 patients with 81 polyps were assessed.The histological subtypes included 50 tubular adenomas,16 hyperplastic polyps,and 15 sessile serrated lesions.The visibility scores for the margins based on WLI,TXI,CE,and TXI+CE were 2.44±0.93,2.90±0.93,3.37±0.74,and 3.75±0.49,respectively.The visibility scores for the surface based on WLI,TXI,CE,and TXI+CE were 2.25±0.80,2.84±0.84,3.12±0.72,and 3.51±0.60,respectively.The visibility scores for the detection and surface on TXI were significantly lower than that on CE but higher than that on WLI(P<0.001).The visibility scores for the margin and surface on TXI+CE were significantly higher than those on CE(P<0.001).In the sub-analysis of adenomas,the visibility for the margin and surface on TXI+CE was significantly better than that on WLI,TXI,and CE(P<0.001).In the sub-analysis of serrated polyps,the visibility for the margin and surface on TXI+CE was also significantly better than that on WLI,TXI,and CE(P<0.001).CONCLUSION TXI+CE enhanced the visibility of the margin and surface compared to WLI,TXI,and CE for colorectal lesions.

Shadow Detection and Compensation for Color Aerial Images

A method for shadow detection and compensation for color aerial images is presented. It is considered that the intensity value of each image pixel is the product of illumination function and ground object reflection, and the shadowed regions on the image are mainly caused by the short of illumination, so the information compensation for the shadowed regions should concentrate on the illumination adjustment of concerned area on the basis of the analysis of whole image. The shadow detection and compensation procedure proposed by this paper consists of four steps.

Improved switching based filter for protecting thin lines of color images

The classical vector median filter (VMF) has been widely used to remove impulse noise from color images. However, since the VMF cannot identify thin lines during the denoising process, many thin lines may be removed out as noise. This serious problem can be solved by a newly proposed filter that uses a noise detector to find these thin lines and then keep them unchanged. In this new approach, the noise detection scheme applied on a current processed pixel is realized through counting the close pixels in its eight neighbor positions and the expanded window to see whether the current pixel is corrupted by impulse noise. Based on the previous outputs, our algorithm can increase the performance in detecting and canceling the impulse noise. Extensive ex- periments indicate that this approach can be used to remove the impulse noise from a color image without distorting the useful information.

A Color Image Encryption Scheme Based on Singular Values and Chaos

The security of digital images transmitted via the Internet or other public media is of the utmost importance.Image encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized entities.This study provides an approach to color image encryption that could find practical use in various contexts.The proposed method,which combines four chaotic systems,employs singular value decomposition and a chaotic sequence,making it both secure and compression-friendly.The unified average change intensity,the number of pixels’change rate,information entropy analysis,correlation coefficient analysis,compression friendliness,and security against brute force,statistical analysis and differential attacks are all used to evaluate the algorithm’s performance.Following a thorough investigation of the experimental data,it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.

A color image encryption algorithm based on hyperchaotic map and DNA mutation

We devise a color image encryption scheme via combining hyperchaotic map,cross-plane operation and gene theory.First,the hyperchaotic map used in the encryption scheme is analyzed and studied.On the basis of the dynamics of hyperchaotic map,a color image encryption scheme is designed.At the end of the encryption process,a DNA mutation operation is used to increase the encoding images’randomness and to improve the encryption algorithm’s security.Finally,simulation experiments,performance analysis,and attack tests are performed to prove the effectiveness and security of the designed algorithm.This work provides the possibility of applying chaos theory and gene theory in image encryption.

Linked color imaging vs Lugol chromoendoscopy for esophageal squamous cell cancer and precancerous lesion screening: A noninferiority study

BACKGROUND Lugol chromoendoscopy(LCE)has served as a standard screening technique in high-risk patients with esophageal cancer.Nevertheless,LCE is not suitable for general population screening given its side effects.Linked color imaging(LCI)is a novel image-enhanced endoscopic technique that can distinguish subtle differences in mucosal color.AIM To compare the diagnostic performance of LCI with LCE in detecting esophageal squamous cell cancer and precancerous lesions and to evaluate whether LCE can be replaced by LCI in detecting esophageal neoplastic lesions.METHODS In this prospective study,we enrolled 543 patients who underwent white light imaging(WLI),LCI and LCE successively.We compared the sensitivity and specificity of LCI and LCE in the detection of esophageal neoplastic lesions.Clinicopathological features and color analysis of lesions were assessed.RESULTS In total,43 patients(45 neoplastic lesions)were analyzed.Among them,36 patients(38 neoplastic lesions)were diagnosed with LCI,and 39 patients(41 neoplastic lesions)were diagnosed with LCE.The sensitivity of LCI was similar to that of LCE(83.7%vs 90.7%,P=0.520),whereas the specificity of LCI was greater than that of LCE(92.4%vs 87.0%,P=0.007).The LCI procedure time in the esophageal examination was significantly shorter than that of LCE[42(34,50)s vs 160(130,189)s,P<0.001].The color difference between the lesion and surrounding mucosa in LCI was significantly greater than that observed with WLI.However,the color difference in LCI was similar in different pathological types of esophageal squamous cell cancer.CONCLUSION LCI offers greater specificity than LCE in the detection of esophageal squamous cell cancer and precancerous lesions,and LCI represents a promising screening strategy for general populations.