Feature Similarity Index Measure Research Articles

An adaptive enhanced human memory algorithm for multi-level image segmentation for pathological lung cancer images

Lung cancer is a critical health issue that demands swift and accurate diagnosis for effective treatment. In medical imaging, segmentation is crucial for identifying and isolating regions of interest, which is essential for precise diagnosis and treatment planning. Traditional metaheuristic-based segmentation methods often struggle with slow convergence speed, poor optimized thresholds results, balancing exploration and exploitation, leading to suboptimal performance in the multi-thresholding segmenting of lung cancer images. This study presents ASG-HMO, an enhanced variant of the Human Memory Optimization (HMO) algorithm, selected for its simplicity, versatility, and minimal parameters. Although HMO has never been applied to multi-thresholding image segmentation, its characteristics make it ideal to improve pathology lung cancer image segmentation.The ASG-HMO incorporating four innovative strategies that address key challenges in the segmentation process. Firstly, the enhanced adaptive mutualism phase is proposed to balance exploration and exploitation to accurately delineate tumor boundaries without getting trapped in suboptimal solutions. Second, the spiral motion strategy is utilized to adaptively refines segmentation solutions by focusing on both the overall lung structure and the intricate tumor details. Third, the gaussian mutation strategy introduces diversity in the search process, enabling the exploration of a broader range of segmentation thresholds to enhance the accuracy of segmented regions. Finally, the adaptive t-distribution disturbance strategy is proposed to help the algorithm avoid local optima and refine segmentation in later stages.The effectiveness of ASG-HMO is validated through rigorous testing on the IEEE CEC'17 and CEC'20 benchmark suites, followed by its application to multilevel thresholding segmentation in nine histopathology lung cancer images. In these experiments, six different segmentation thresholds were tested, and the algorithm was compared to several classical, recent, and advanced segmentation algorithms. In addition, the proposed ASG-HMO leverages 2D Renyi entropy and 2D histograms to enhance the precision of the segmentation process.Quantitative result analysis in pathological lung cancer segmentation showed that ASG-HMO achieved superior maximum Peak Signal-to-Noise Ratio (PSNR) of 31.924, Structural Similarity Index Measure (SSIM) of 0.919, Feature Similarity Index Measure (FSIM) of 0.990, and Probability Rand Index (PRI) of 0.924. These results indicate that ASG-HMO significantly outperforms existing algorithms in both convergence speed and segmentation accuracy. This demonstrates the robustness of ASG-HMO as a framework for precise segmentation of pathological lung cancer images, offering substantial potential for improving clinical diagnostic processes.

Static X-ray differential phase contrast imaging via bidirectional-misalignment grating

Grating-based X-ray differential phase contrast imaging (GB-DPCI) offers excellent imaging contrast for soft tissues. Limited by the phase stepping of gratings, the conventional GB-DPCI leads to a high radiation dose. This study presents a static GB-DPCI method employing an analyzer grating with a misaligned structure in both vertical and horizontal directions. A signal retrieval algorithm is developed for this method to permit the use of neighboring pixels on the detector to approximate multiple phase stepping positions, thereby obtaining the phase contrast signal in a single exposure. This study reduces high radiation doses and time-consumption caused by phase stepping. The presented method is validated with datasets from a laboratory X-ray tube and synchrotron radiation. Considering synchrotron results as example, minimal improvements in Feature Similarity Index Measure are 2.48%, 6.31%, and 7.06% for absorption, dark field, and phase contrasts, and in Information-Weight Structure Similarity Index Measure are 3.24%, 16.07%, and 8.61%.

Incorporation of Structural Similarity Index and Regularization Term into Neighbor2Neighbor Unsupervised Learning Model for Efficient Ultrasound Image Data Denoising

Medical ultrasound imaging is extensively employed for diagnostic purposes. However, image quality remains a major obstacle to achieving greater accuracy. Conventional supervised deep learning denoising methods often rely on matched noise-free and noisy image pairs, which can be highly challenging in practical ultrasound applications. Moreover, due to the limitations associated with independent noise, existing unsupervised denoising methods such as Neighbor2Neighbor are unable to efficiently address correlated noise in ultrasound images. Meanwhile, these methods utilize a random neighborhood downsampling technique, frequently resulting in pixel loss. Hence, this study proposes a novel Neighbor2Neighbor algorithm, which reconstructs ultrasound images by improving the downsampling approach. Moreover, it incorporates a structural similarity index and a regularization term, thereby enhancing its ability to suppress both independent and correlated noise. Extensive experiments on an ultrasound image dataset demonstrate that the proposed Neighbor2Neighbor algorithm outperforms the state-of-the-art baseline algorithms in peak signal-to-noise ratio (PSNR), mean structural similarity index measure (MSSIM), feature similarity index measure (FSIM), and edge preservation index (EPI).

Image Contrast Enhancement Using Artificial Hummingbird Algorithm

Abstract: Image enhancement plays an important role in medical imaging, satellite image processing, and computer vision. These fields require high-quality images to improve the visibility of details, which is essential for accurate analysis and interpretation. Contrast adjustment, histogram equalization, and gamma correction are the traditional image enhancement techniques that are widely used. However, these methods often fall short, particularly when applied uniformly across diverse images, as they may not always yield the best results. We provide a unique picture enhancing method based on the Artificial Hummingbird Algorithm (AHA) to overcome these drawbacks. The AHA is inspired by the foraging behaviour of hummingbirds, which are known for their efficient and adaptive search strategies. This bio-inspired algorithm leverages strategies to optimize the gamma correction parameter, aiming to enhance image contrast and overall visual quality. The aim is to optimize the gamma correction parameter to maximize fitness values derived from several image quality metrics, including Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM), and Peak Signal-to-Noise Ratio (PSNR). The AHA operates through a population-based approach, where each individual in the population represents a potential solution with a specific gamma value. Initially, a population of random gamma values is generated, and each value is used to enhance the image, followed by evaluating its quality using the combined fitness function. The algorithm uses three distinct foraging strategies to efficiently explore the solution space: guided, territorial and migration foraging. Guided foraging helps steer the search towards promising areas, while territorial foraging refines the local search. Migration foraging periodically resets some individuals to avoid premature convergence and ensure a comprehensive exploration of the solution space. Through iterative adaptation and evolution, the AHA continuously updates and refines the best gamma value found, leading to significantly enhanced images. Experimental results demonstrate that the AHA-based technique offers substantial improvements in image quality, with better contrast, sharper details, and higher scores in the quality metrics compared to traditional methods, making it a robust and efficient solution for high-quality image processing tasks.

Application of Multi-Level Thresholding Fuzzy Entropy and Differential Evolution Techniques to Medical Image Compression

Each storage medium possesses a capacity that is vital to technological progress in supporting internet speed for downloading extensive data. Effective storage media management includes compressing image data to optimize available space utilization for efficient usage. Digital processing of image data for each patient over a specific duration is crucial. This study proposes medical image compression utilizing multi-level thresholding employing the fuzzy entropy and differential evolution (FE-DE) algorithms. The compression results are subsequently assessed using the Peak Signal to Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM) to appraise the algorithm's performance and the quality of the compressed images. Employing Fuzzy Compression yields increasing PSNR, SSIM, and FSIM values as the threshold level rises, with the highest values achieved at threshold level 40. The increase in value is in line with the visual quality and compressed image produced.

A New Method Used to Enhance the SPAIR Image of the Spine MRI.

Magnetic resonance imaging (MRI) is a handy diagnostic tool for orthopedic disorders, particularly spinal and joint diseases. The lumbar intervertebral disc is visible in the T1 and T2 weight sequences of the spine MRI, which aids in diagnosing lumbar disc herniation, lumbar spine tuberculosis, lumbar spine tumors, and other conditions. The lumbar intervertebral disc cannot be seen accurately in the Spectral Attenuated Inversion Recovery (SPAIR) due to weaknesses in the fat and frequency offset parameters, which is not conducive to developing the intelligence diagnosis model of medical image. In order to solve this problem, we propose a composite framework, which is first to use the contrast limited adaptive histogram equalization (CLAHE) method to enhance the SPAIR image contrast of the spine MRI and then use the non-local means method to remove the noise of the image to ensure that the image contrast is uniform without losing details. We employ the Information Entropy (IE), Peak signal-to-noise ratio (PSNR), and feature similarity index measure (FSIM) to quantify image quality after enhancement by the composite framework. The outcomes of the experiments' output images and quantitative data indicate that our composite framework is better than others.

Generative adversarial networks with attentional multimodal for human face synthesis

<p><span>Face synthesis and editing has increased cumulative consideration by the improvement of generative adversarial networks (GANs). The proposed attentional GAN-deep attentional multimodal similarity modal (AttnGAN-DAMSM) model focus on generating high-resolution images by removing discriminator components and generating realistic images from textual description. The attention model creates the attention map on the image and automatically retrieves the features to produce various sub-areas of the image. The DAMSM delivers fine-grained image-text identical loss to generative networks. This study, first describe text phrases and the model will generate a photorealistic high-resolution image composed of features with high accuracy. Next, model will fine-tune the selected features of face images and it will be left to the control of the user. The result shows that the proposed AttnGAN-DAMSM model delivers the performance metrics like structural similarity index measure (SSIM), feature similarity index measure (FSIM) and frechet inception distance (FID) using CelebA and CUHK face sketch (CUFS) dataset. For CelebFaces attribute (CelebA) dataset, the SSIM achieves 78.82% and for CUFS dataset, the SSIM achieves 81.45% which ensures accurate face synthesis and editing compared with existing methods such as GAN, SuperstarGAN and identity-sensitive GAN (IsGAN) models.</span></p>

Comparative Analysis on Image Compression Algorithm Based on Block Truncation Coding

Block Truncation Coding is a quick and effective method for compressing images, because it employs a non-parametric one-bit or two-bit quantization procedure that adapts to the features of the image. This paper describes and compares the performance of BTC and BTC based techniques such as Enhanced Block Truncation Coding (EBTC), Modified Block Truncation Coding (MBTC), and Adaptive Block Truncation Coding – Edge Quantization (ABTC – EQ). These techniques were applied on the general images and their performance were measured using the metrics such as Peak Signal Noise Ratio (PSNR), Structured Similarity Index Measure (SSIM), Weighted Peak Signal Noise Ratio (WPSNR), Feature Similarity Index Measure (FSIM) and Compression Ratio (CR).

Joint Bi-Modal Tomographic Reconstruction from Observations with Missing Data

A method for tomographic reconstruction of bi-modal data sets using cross modal regularization was developed. The method constrains the Simultaneous Algebraic Reconstruction Technique by using clustering-based segmented images and quality controlled fused images, to jointly regularize the reconstruction of structures imaged with different methods. The joint regularized reconstruction method was tested using a synthetic X-Ray and neutron data set with a mix of attenuation driven data starvation. Assuming consistent material boundaries in the different imaging modalities, it was shown that improvements in terms of the Feature Similarity Index Measure are made compared to a traditional SART-TV reconstruction. UK Ministry of Defence ©Crown Owned Copyright 2023/AWE

Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression.

The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel "Flicker Test Software" is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

Image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) for low exposure image enhancement.

In this paper, a novel image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) technique is proposed for the enhancement of low exposure images. The proposed method involves, computation of the histogram which includes a new approach of image sub-division, enhancement controlling mechanism, modification of probability density function (PDF) and histogram equalization (HE). The original histogram is segmented into sub-histograms based on exposure threshold and mean, to preserve the brightness and entropy. Then, individual sub-histogram is clipped separately to control the enhancement rate. For enhancing the visual quality, HE is applied to individual sub-histogram using the modified PDF. The experimental results show that, the proposed ISQCAHE method avoids the unpleasant artifacts effectively and provide a natural appearance to the enhanced image. It is simple, adaptive and performs superior than other techniques in terms of visual quality, absolute mean brightness error, entropy, Natural image quality evaluation, brightness preservation, structure similarity index measure and feature similarity index measure.

Particle Swarm Optimization and Salp Swarm Algorithm for the Segmentation of Diabetic Retinal Blood Vessel Images.

In recent years, the incidence of diabetes has been increasing year by year. Since most of the fundus lesions are located near blood vessels, the image information is complex, and the end vessels are difficult to identify. So, a new segmentation method of diabetic retinal vessel images based on particle swarm optimization and salp swarm algorithm is proposed. This paper uses a Gaussian filter to enhance the main blood vessels, and a top-bot hat transform is used to strengthen the end vessels. The preprocessing process is completed by combining and reconstructing the two images through a normalization operation. The improved particle swarm optimization and salp swarm algorithms perform multi-threshold segmentation on the preprocessed vessel images. The best fit value, Structural Similarity Index Measure, Peak Signal to Noise Rati, feature similarity index measure, sensitivity, accuracy, regional consistency, Dice coefficient, Jaccard similarity, and Shannon entropy are selected for comprehensive evaluation and analysis. The results showed that this paper's improved particle swarm-salp swarm algorithm for segmenting diabetic retinal blood vessel images is more efficient, and the threshold is better. The vascular segmentation method in this paper is applied in medical image processing, which improves the accuracy of medical image processing and reduces the computational effort.

Evaluation of Weighted Nuclear Norm Minimization Algorithm for Ultrasound Image Denoising

Anatomical structures manifested in ultrasound (US) images are crucial in efficient disease diagnosis. This modality has been used to analyze different tissue properties, such as blood flow, in-depth tissue motion, and elasticity. Analysis of these US images is posing a critical challenge, as these images are corrupted with noise primarily induced during acquisition. The biological structures intended to be investigated need to be detected, enhanced, and preserved during image processing-based diagnosis. US-based common carotid artery (CCA) images were considered in this study, and five denoising techniques were explored for noise removal after converting the images to grayscale to identify efficient preprocessing for effective diagnosis. Furthermore, filtered images were subjected to different entropy-inspired segmentation for qualitative validation and to segment the CCA. The objective of this paper is a deliberate attempt to investigate the possible use of edge- and structure-preserving filtering techniques to segment tissues of interest. The weighted nuclear norm minimization (WNNM) approach appears to be effective in removing noise and simultaneously preserving the sensitive structures. Quantitative validation with peak signal to noise ratio (PSNR), structural symmetry index measure (SSIM), and feature similarity index measure (FSIM) found to be 27.84 ± 1.04 dB; 0.76 ± 0.01 and 0.87 ± 0.01 were observed to be superiorly high with WNNM filtering. The input image and the filtered image histograms are also compared for qualitative validation. The key finding in this study can be attributed to the ability to remove noise from US images corrupted with noise while preserving the anatomical details. Furthermore, it can be hypothesized that the anatomical structures under the influence of noise can be efficiently preprocessed and can be fed as a viable image towards segmentation followed by recognition and morphological inference.

Histogram modification in adaptive bi-histogram equalization for contrast enhancement on digital images

In this paper, a novel histogram modification-based bi histogram equalization (HE) approach for contrast enhancement on digital images is presented. At first, a power-logarithm transformation function is used to change the histogram of the input image. The logarithm operation reduces the input histogram’s excessive peaks, while the power function restores the histogram structure. The adjusted histogram is then separated into two sub-histograms at the threshold limit, using the input image’s minimum intensity and standard deviation. Sub-histograms are clipped based on their individual plateau limit parameters, which are based on the median value of the individual sub-histogram, to control the over-enhanced outcomes. The clipped pixels are then redistributed evenly across the histogram’s non-empty bins. Finally, the intended outcome is achieved by applying the HE procedure on the updated individual sub-histogram. Simulation results show that the proposed HE approach successfully improves the visual quality of the images. Quantitative measurements such as entropy, feature similarity index measure (FSIM), spectral residual similarity index measure (SR-SIM), gradient magnitude similarity deviation (GMSD), visual saliency-induced index (VSI) and multi scale structural similarity index measure (MS-SSIM) efficiently confirm the effectiveness of the suggested method when compared with the existing enhancement techniques. Furthermore, a comparative study of the different approaches is performed using a dataset of 300 images, demonstrating the superiority of the suggested method over the other state-of-the-art techniques.

Comparison of Image Enhancement Algorithms for Improving the Visual Quality in Computer Vision Application

Computer vision has its numerous real-world applications on Visual Object Tracking which includes human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security, human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. The factors affecting the tracking process is due to low illumination, haze and cloudy environment and noisy environment. In this paper, we aim to extensively review the latest trends and advances in adaptive enhancement algorithm and evaluate the performance using Full reference like, SSIM (Structure Similarity Index Measure), MS-SSIM (Multi-scale Structure Similarity Index Measure), ESSIM (Edge Strength Structural Similarity Index), FSIM (Feature Similarity Index Measure), VIF (Visual Information Fidelity), CW-SSIM (complex wavelet structural similarity), UQI (Universal Quality Index), IEF (Image Enhancement Factor), IQI (Image Quality Index), EME (Enhancement Measurement Error), CVSI (Contrast and Visual Salient Information), MCSD (Multiscale contrast similarity deviation), NQM (Noise Quality Measure), Gradient Magnitude Similarity Mean (GMSM), Gradient Magnitude Similarity Deviation (GMSM) and no-reference image quality measures Perception based Image Quality Evaluator (PIQE), Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), Average Gradient (AG), Contrast, Information Entropy (IE), Lightness order Error (LOE). The main purpose of adaptive image enhancement is to smooth the uniform area and sharpen the border of an image to improve its visual quality. In this paper, fourteen image enhancement algorithms were tested on LoL dataset to benchmark the time taken to process them and their output quality was evaluated. Results from this study will give insights to image analysts for selecting image enhancement algorithms which acts as a pre- processing stage for Visual object Tracking.

A Hybrid Digital Image Watermarking Scheme Incorporating DWT, DFT, DCT and SVD Transformations

Digital watermarking provides copyright protection and proof of ownership by inserting watermark metadata as owner’s identity in digital documents to prevent authenticity and copyright violations. The paper introduces a new hybrid image watermarking scheme by attaching multiple copies of watermarks in carrier image. The new scheme utilizes the advantages of DWT, DFT, DCT and SVD transformations to offer stable resistance in protecting watermark contents from various external attacks. The proposed scheme uses Haar wavelet, Fourier, Onion Peel Decomposition, DCT, zigzag ordering and SVD transforms to decompose the carrier image in to four levels to maintain imperceptibility in the watermarked images. The algorithm attaches replicas of watermark frequency blocks in all frequency components of host image to provide better robustness against external deprivations in watermarked images. The proposed algorithm also provides the increased probability of extracting at least one undamaged replica of watermark even when other frequencies are damaged by external attacks. The improved experimental results of the proposed scheme in terms of visual analysis and quantitative metrics on different images with different experimental set up demarcate that the proposed watermarking scheme provides stable performance in generating better watermarked images. It is experimentally found that the new scheme produces high quality watermarked images with an average of 7.62% lesser Mean Absolute Error (MAE) and increased Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index Measure (MSSIM) and Feature Similarity Index Measure (FSIM) of 5.02 %, 4.37 %, and 2.37 % respectively than the next best algorithms when simulated with 20 sets of watermark and cover images. The watermark images extracted by the proposed algorithm from extremely distorted watermarked images are with better visual and objective values than other methods used in the comparative study. Simulation analysis on 20 sets of watermark and cover images with 30 types of potential attacks reveals that the extracted watermark images through the proposed scheme produces an average of 5.62%, 6.37%, 5.75% improved Pearson Correlation Coefficients (PCC), Number of Changing Pixel Rate (NPCR) and the Unified Averaged Changed Intensity (UACI) values respectively than the next best algorithms used in the comparative study.

Denoising of pre-beamformed photoacoustic data using generative adversarial networks.

We have trained generative adversarial networks (GANs) to mimic both the effect of temporal averaging and of singular value decomposition (SVD) denoising. This effectively removes noise and acquisition artifacts and improves signal-to-noise ratio (SNR) in both the radio-frequency (RF) data and in the corresponding photoacoustic reconstructions. The method allows a single frame acquisition instead of averaging multiple frames, reducing scan time and total laser dose significantly. We have tested this method on experimental data, and quantified the improvement over using either SVD denoising or frame averaging individually for both the RF data and the reconstructed images. We achieve a mean squared error (MSE) of 0.05%, structural similarity index measure (SSIM) of 0.78, and a feature similarity index measure (FSIM) of 0.85 compared to our ground-truth RF results. In the subsequent reconstructions using the denoised data we achieve a MSE of 0.05%, SSIM of 0.80, and a FSIM of 0.80 compared to our ground-truth reconstructions.

ANALISA METODE SHANNON ENTROPY DAN DIFFERENTIAL EVOLUTION UNTUK KOMPRESI GAMBAR

One of the most important phases in image storage is compression. Most current imagecompression methods are spatial. In this article, we present an image compression techniquebased on Multi-level Thresholding. The grayscale images are divided into groups based on thenet probabilistic division. To determine the grouping uncertainty, Shannon Entropy is used.Optimization methods have also been added to obtain more optimal settings. DifferentialEvolution is an optimization technique. Image histogram is a graph that depicts the distributionof pixel intensity values of an image. Image compression performance measurement wasmeasured using FSIM (Feature Similarity Index Measure) and SSIM (Structural Similarity IndexMeasure).

Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding

AbstractImage segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient‐based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC‐SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.