Image Denoising Using Wavelet Transform Based Flower Pollination Algorithm

Image and video compression aims at finding an optimal trade-off between rate and distortion. This is done through Rate-Distortion Optimization (RDO) in traditional en-coders with the use of Image Quality Assessment (IQA) metrics. While it is known that most IQA metrics are designed to be correlated with human perception, there is no evidence that this observation can be generalized in a Video Coding for Machines (VCM) context, where the receiver is not a human anymore but a machine. In this paper, we propose an evaluation protocol to measure the correlation level between conventional Full-Reference (FR) IQA metrics and machine perception through the semantic segmentation vision task. Experiments showed a relatively low correlation between them when measured on the block-level. This observation implies the need of RDO algorithms that are better suited for Machine-to-Machine (M2M) communications. In order to facilitate the emergence of IQA metrics that better reflect machine perception, the code and dataset used to perform this study is made freely available at https://github.com/albmarie/iqa_m2m_segmentation.

영상 처리 및 컴퓨터 비전 분야에 있어서, 평균 제곱 오차(Mean Squared Error: MSE)는 좋은 수학적 특성(예를 들어, 척도성(metricability), 미분가능성(differentiability) 및 볼록 성질(convexity))을 가짐으로 인해 많은 영상 화질 최적화 문제의 객관적 척도로 사용되어 왔다. 그러나 MSE가 영상의 왜곡 신호에 대한 시각적 인지 화질과 상관도가 높지 않다는 것이 알려지면서, 이를 해결하기 위해 위에서 언급한 좋은 수학적 특성과 높은 영상 화질 예측 성능을 동시에 가지는 객관적 영상 화질 측정(Image Quality Assessment: IQA)척도가 활발히 연구되어 왔다. 비록 최근 제안된 좋은 수학적 성질을 만족시키는 IQA 척도들은 MSE와 비교하여 매우 향상된 주관적 화질 예측 성능을 보이지만, 상대적으로 높은 계산 복잡도를 가진다. 본 논문은 이를 해결하기 위해, 단순 라플라스 연산자를 이용한 좋은 수학적 특성을 가지는 새로운 IQA 척도를 제안한다. 제안 IQA 방법에 도입한 단순 라플라스 연산자는 인간 시각 체계의 망막에서의 광도 자극에 대한 시신경 반응을 효과적으로 모사할 뿐만 아니라 계산이 매우 단순하기 때문에, 제안 IQA 척도는 단순 라플라스 연산자를 사용하여 매우 빠른 계산 속도와 높은 주관적 화질 점수 예측력을 확보하였다. 제안 IQA 척도의 효과를 검증하기 위해, 최신 IQA 척도들과 광범위한 성능비교 실험을 수행하였다. 실험 결과, 제안하는 IQA 척도는 모든 테스트 IQA 척도들 중 MSE를 제외하고 가장 빠른 처리 속도를 보였을 뿐만 아니라, 가장 높은 주관적 화질예측 성능을 보였다. In image processing and computer vision fields, mean squared error (MSE) has popularly been used as an objective metric in image quality optimization problems due to its desirable mathematical properties such as metricability, differentiability and convexity. However, as known that MSE is not highly correlated with perceived visual quality, much effort has been made to develop new image quality assessment (IQA) metrics having both the desirable mathematical properties aforementioned and high prediction performances for subjective visual quality scores. Although recent IQA metrics having the desirable mathematical properties have shown to give some promising results in prediction performance for visual quality scores, they also have high computation complexities. In order to alleviate this problem, we propose a new fast IQA metric using a simple Laplace operator. Since the Laplace operator used in our IQA metric can not only effectively mimic operations of receptive fields in retina for luminance stimulus but also be simply computed, our IQA metric can yield both very fast processing speed and high prediction performance. In order to verify the effectiveness of the proposed IQA metric, our method is compared to some state-of-the-art IQA metrics. The experimental results showed that the proposed IQA metric has the fastest running speed compared the IQA methods except MSE under comparison. Moreover, our IQA metric achieves the best prediction performance for subjective image quality scores among the state-of-the-art IQA metrics under test.

Noise Removal in medical images is a requisite pre-processing step for medical image analysis. In this paper, we present three deep learning architectures: Convolutional Auto-encoders, U-Net and a Double U-Net architecture utilizing convolution layers for medical Image De-noising. We use PSNR(Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Metric) to compare the results of the networks.In our experiments, the variant of the U-Net architecture implemented performed better than the two architectures in terms of its SSIM.

A fusion-domain color image watermarking based on Haar transform and image correction

Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric 99m Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time images from short-acquisition-time pediatric 99m Tc-DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy. One hundred and fifty-five cases that underwent pediatric 99m Tc-DMSA planar imaging as dynamic data for 10min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full-time images from short-time images. We used the normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full-time images. In addition, the renal uptake of 99m Tc-DMSA was calculated, and the difference in renal uptake from the reference full-time images was assessed using scatter plots with Pearson correlation and Bland-Altman plots. The predicted full-time images from the deep learning models showed a significant improvement in image quality compared to the short-time images with respect to the reference full-time images. In particular, the predicted full-time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4-0.5] %) and the highest PSNR (55.4 [54.7-56.1] dB) and SSIM (0.997 [0.995-0.997]). For renal uptake, an extremely high correlation was achieved in all short-time and three predicted full-time images (R2 >0.999 for all). The Bland-Altman plots showed the lowest bias (-0.10) of renal uptake in ResUnet, while short-time images showed the lowest variance (95% confidence interval: -0.14, 0.45) of renal uptake. Our proposed method is capable of producing images that are comparable to the original full-acquisition-time images, allowing for a reduction of acquisition time/injected dose in pediatric 99m Tc-DMSA planar imaging.

Due to various reasons an image may get distorted. To measure the perceptual distortions, image quality assessment (IQA) metrics are required. Peak-signal-to-noise-ratio (PSNR) is one of the popular available IQA metric. It is usually measured by computing distortion in terms of mean-squared-error (MSE) between the reference image and its distorted version. Mean-squared-error (MSE) is measured over the entire image by giving equal weight to each corresponding pixel difference of the reference and distorted image. However, as per investigation done in this paper, it is found that instead of calculating MSE/PSNR on overall image, PSNR measured over the region of maximum distortion only is a better choice. The PSNR computed over the most distorted region (PSNR-MDR) reflects the image quality better than it computed over entire image. The PSNR-MDR is not only superior to the PSNR but also competitive to celebrated metric, SSIM (Structural Similarity Index).

Focusing on enhancing the security and privacy of electronic medical images (EMI) which have varying properties and larger in number before they are stored or transmitted by hospitals, labs, and researchers or while they reside in third party storage. EMI should be anywhere, anytime accessible for referencing and medical diagnosis. Therefore, should be accessible in real-time through real-time applications (RTA). This research work uses a large data set having 5856 X-ray images of a total size of 1.6 GB in JPEG format each of these X-ray images vary in properties such as size and dimensions. Two methods have been implemented for enhancing security and privacy Edge-Based Steganography (EBS) and Block-Based Steganography (BBS) in the region of no interest (RONI) of the normalized cover image. Both methods are compared for enhancement concerning steganography properties such as payload capacity, imperceptibility, security, reduction in computational time, and lesser computational complexity which increases their applicability for RTA. RTA makes EMI accessible on heterogeneous devices, therefore, increasing its scalability and usage. This paper performs comparative evaluation based on standardized tests such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), Coefficient of Variation (CV), and Pearson Correlation (R), etc. Results show that both methods achieved a 100% embedding rate, SSIM value close to 1, and low average MSE value of 0.005 for EBS secret images and 0.003 for BBS secret images. Results indicate both methods are capable of hiding large data sets within minutes therefore making them both suitable for RTA while the EBS method takes 2.3 minutes which is less than BBS’s 3.6 minutes encoding time for 5856 images. Therefore, the EBS method was found more suitable than the BBS method based on computational time.

Style transfer is an optimizing technique that aims to blend style of input image to content image. Deep neural networks have previously surpassed humans in tasks such as object identification and detection. Deep neural networks, on the contrary, had been lagging behind in generating higher quality creative products until lately. This article introduces deep-learning techniques, which are vital in accomplishing human characteristics and open up a new world of prospects. The system employs a pre-trained CNN so that the styles of the provided image is transferred to the content image to generate high quality stylized image. The designed systems effectiveness is evaluated based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metrics (SSIM), it is noticed that the designed method effectively maintains the structural and textural information of the cover image.

Image enhancement is better achieved when fractional-order masks are used rather than integer-order ones, this is due to the flexibility of fractional-order parameters control. This paper proposes a combination of fractional-order masks to be used in parallel as double filters system structure to improve image enhancement rather than using a single-stage filter. Various performance metrics are used in this work to evaluate the proposed system, such as Information Entropy (IE), Average Gradient (AG), Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR). Based on visual as well as numerical results, it is found that the combination of two double masks is superior to the single fractional-order system in terms of enhancing texture and edges.

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

View synthesis brings geometric distortions which are not handled efficiently by existing image quality assessment metrics. Despite the widespread of 3-D technology and notably 3D television (3DTV) and free-viewpoints television (FTV), the field of view synthesis quality assessment has not yet been widely investigated and new quality metrics are required. In this study, we propose a new full-reference objective quality assessment metric: the View Synthesis Quality Assessment (VSQA) metric. Our method is dedicated to artifacts detection in synthesized view-points and aims to handle areas where disparity estimation may fail: thin objects, object borders, transparency, variations of illumination or color differences between left and right views, periodic objects... The key feature of the proposed method is the use of three visibility maps which characterize complexity in terms of textures, diversity of gradient orientations and presence of high contrast. Moreover, the VSQA metric can be defined as an extension of any existing 2D image quality assessment metric. Experimental tests have shown the effectiveness of the proposed method.

Recently, various objective image quality assessment (IQA) metrics have been proposed for measuring the quality of a captured image. Moreover, IQA databases have been made available to enable researchers develop and test IQA metrics. For instance, the TID2013 database was used in the experiments conducted in this study. It contains many test images obtained from 25 reference images, each having 24 types of distortion and five levels of noise for each distortion. However, estimating a subjective evaluation score for many types of noise is a difficult task. Therefore, this study proposes an objective IQA framework for evaluating noise clusters using image features. Experimental results confirmed the effectiveness of the proposed framework.

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

Digital image processing is used in many real-world applications and in the field of industrial automation. Diversifying the difficult environment has a huge effect on images by directly affecting the decision derived from image analysis and limits the acceleration of industrial automation. Environmental parameters such as light, time (day or night), and weather conditions play a vital role in the quality of photos captured in an outdoor environment. The usage of images captured from outdoor environments makes it difficult to recognize entities. For this, there exists a work to detect the amount of smoke in foggy images using a deep neural network. However, to our knowledge, no techniques are currently available that objectively assess the impact of environmental parameters such as rain, shadow, darkness, and fog, all of which can have a dramatic impact on image quality. In the data mining process, there are many approaches for numerical data; however, the lack of quantitative techniques for the preparation and understanding of image data can make image analysis a very tedious activity.To address this research gap, we present an innovative technique for evaluating the impact of environmental parameters on the image quality, compared with the quality affected by the JPEG image compression technique and the different levels of Gaussian noise. In addition to the impact of fog, we also analyse the effect of rain, shadow, and darkness on the images by utilizing image quality assessment metrics like Mean Squared Error (MSE), Peak signal-to-noise ratio (PSNR), Structural Content (SC), Oriented FAST and rotated BRIEF (ORB) and Structural Similarity Index (SSIM). We use foggy and traffic sign recognition datasets which have different levels of environmental impact. The quality values produced by SSIM are consistent with the different levels of impacts, JPEG image quality, and Gaussian noise. As might be anticipated, rain and darkness have a substantial impact on the image quality. The values produced by SSIM and their corresponding values for Gaussian noise can be used to quantitatively assess the image quality, thereby improving both image understanding and image preparation.

Digital images, particularly medical images, are prone to various noise attacks, such as additive, multiplicative, etc. Salt and pepper noise (SPN) is a type of multiplicative noise that degrades image quality and complicates identification, potentially leading to inaccurate diagnoses. Removing SPN is crucial yet challenging as noise removal can also eliminate important edge details. This study addresses the challenge of removing SPN without losing edge details. Existing SPN filtering algorithms, such as Average Filter (AF), Median Filter (MF), Switched MF (SMF), Adaptive MF (AMF), and Decision-based MF (DMF), perform poorly under high SPN attacks. We propose two effective SPN filters, CA_SPN1 and CA_SPN2, under the framework of Cellular Automata (CA) and MF. CA_SPN1 excels at high SPN levels, while CA_SPN2, a combination of CA_SPN1 and AMF, is effective across all SPN levels. Quality evaluations using Image Quality Assessment (IQA) metrics – Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Figure of Merit (FOM), and Perceptual Similarity (PSIM) – demonstrate that the proposed algorithms restore images more effectively while preserving edge details compared to state-of-the-art algorithms. Furthermore, experimental results confirm the superior restoration quality of the proposed filters, particularly under high SPN conditions.