Peak Signal To Noise Ratio Index Research Articles

An Improved Variational-Based Model for Denoising and Segmentation of Vector-Valued Images

Preserving important features such as edges is one of the main concerns in models for denoising and segmenting vector-valued (colour) images. The Rudin-Osher-Fatemi (ROF) model is a well-known variational-based image denoising model that is capable of reducing image noise while preserving image edges. However, the ROF model is not formulated for denoising colour images and is less effective in preserving corners and weak edges. On the other hand, a variational-based selective segmentation model for colour images called the selective distance segmentation (DSS2) model has recently been proposed, which can effectively partition or extract a specific object in an image. However, the DSS2 model has problems in segmenting colour images with noise, which may result in poor segmentation. Therefore, in this research, we first modify the ROF model to denoise vector-valued images by including the edge detector and extending the formulation into a vector-valued framework. Second, we reformulate the DSS2 model by incorporating the modified ROF model as a new fitting term in the DSS2 model. Peak signal-to-noise ratio (PSNR) is used to measure the image quality, while Jaccard and Dice similarity index are used to evaluate the segmentation quality. The comparison between our proposed model and existing model shows that our model is more effective as indicated by higher PSNR, Jaccard and Dice similarity index values.

SwinWave-SR: Multi-scale lightweight underwater image super-resolution

The resource-limited nature of underwater vision equipment leads to poor, otherwise low-resolution information affecting the downstream underwater robotics and ocean engineering tasks. Underwater Image Enhancement (UIE) methods have emerged, particularly Super-Resolution (SR), to tackle the aforementioned challenge by restoring the corresponding low-resolution image to a high-quality counterpart. Vision Transformers (ViTs) have recently been employed for SR tasks thanks to their superior performance over mainstream convolution neural networks. The success of ViTs is largely due to their self-attention mechanism; however, they may encounter challenges in dealing with severe and unpredictable degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) variants such as the Swin transformers have overcome that challenge by preserving long-range dependencies over multi-scale feature hierarchies through evolving channel capacity. MViTs tend to induce spatial efficiency through classical down-sampling, such as average pooling over key/values, which results in an inevitable loss of high-frequency components. To address this lack, in the current work, we propose a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale SR for underwater images. Our proposed algorithm is based on Swin transformer consisting of a wavelet block to restrict the information drop by downsampling in an invertible fashion. Consequently, the key components are preserved to assist self-attention learning while reducing its computational cost simultaneously. To further complement it, we explore a prominent compression regime, namely the Lottery Ticket Hypothesis (LTH), to discover a lightweight sub-network with competitive performance to its original model by reducing computational costs up to 70.44%. Overall, SwinWave-SR improves peak signal-to-noise ratio (PSNR) by 0.95 dB ∼ 2.23 dB compared to the state-of-the-art SwinIR while reducing the number of parameters by 29.56% and the calculation cost by 18.734%. Experimental results show that the proposed SwinWave-SR method outperforms the state-of-the-art SR methods on four benchmark underwater datasets and significantly improves PSNR and structural similarity index (SSIM).

Fusing Satellite Images Using ABC Optimizing Algorithm

Fusing satellite (remote sensing) images is an interesting topic in processing satellite images. The result image is achieved through fusing information from spectral and panchromatic images for sharpening. In this paper, a new algorithm based on based the Artificial bee colony (ABC) algorithm with peak signal-to-noise ratio (PSNR) index optimization is proposed to fusing remote sensing images in this paper. Firstly, Wavelet transform is used to split the input images into components over the high and low frequency domains. Then, two fusing rules are used for obtaining the fused images. The first rule is “the high frequency components are fused by using the average values”. The second rule is “the low frequency components are fused by using the combining rule with parameter”. The parameter for fusing the low frequency components is defined by using ABC algorithm, an algorithm based on PSNR index optimization. The experimental results on different input images show that the proposed algorithm is better than some recent methods.

An efficient image encryption scheme for healthcare applications.

In recent years, there has been an enormous demand for the security of image multimedia in healthcare organizations. Many schemes have been developed for the security preservation of data in e-health systems however the schemes are not adaptive and cannot resist chosen and known-plaintext attacks. In this contribution, we present an adaptive framework aimed at preserving the security and confidentiality of images transmitted through an e-healthcare system. Our scheme utilizes the 3D-chaotic system to generate a keystream which is used to perform 8-bit and 2-bit permutations of the image. We perform pixel diffusion by a key-image generated using the Piecewise Linear Chaotic Map (PWLCM). We calculate an image parameter using the pixels of the image and perform criss-cross diffusion to enhance security. We evaluate the scheme’s performance in terms of histogram analysis, information entropy analysis, statistical analysis, and differential analysis. Using the scheme, we obtain the average Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) values for an image of size 256 × 256 equal to 99.5996 and 33.499 respectively. Furthermore, the average entropy is 7.9971 and the average Peak Signal to Noise Ratio (PSNR) is 7.4756. We further test the scheme on 50 chest X-Ray images of patients having COVID-19 and viral pneumonia and found the average values of variance, PSNR, entropy, and Structural Similarity Index (SSIM) to be 257.6268, 7.7389, 7.9971, and 0.0089 respectively. Furthermore, the scheme generates completely uniform histograms for medical images which reveals that the scheme can resist statistical attacks and can be applied as a security framework in AI-based healthcare.

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.

Color Image Generation from Range and Reflection Data of LiDAR.

Recently, it has been reported that a camera-captured-like color image can be generated from the reflection data of 3D light detection and ranging (LiDAR). In this paper, we present that the color image can also be generated from the range data of LiDAR. We propose deep learning networks that generate color images by fusing reflection and range data from LiDAR point clouds. In the proposed networks, the two datasets are fused in three ways—early, mid, and last fusion techniques. The baseline network is the encoder-decoder structured fully convolution network (ED-FCN). The image generation performances were evaluated according to source types, including reflection data-only, range data-only, and fusion of the two datasets. The well-known KITTI evaluation data were used for training and verification. The simulation results showed that the proposed last fusion method yields improvements of 0.53 dB, 0.49 dB, and 0.02 in gray-scale peak signal-to-noise ratio (PSNR), color-scale PSNR, and structural similarity index measure (SSIM), respectively, over the conventional reflection-based ED-FCN. Besides, the last fusion method can be applied to real-time applications with an average processing time of 13.56 ms per frame. The methodology presented in this paper would be a powerful tool for generating data from two or more heterogeneous sources.

Performance Analysis of Image Processing in Hexagonal Grid Based on Image Compression, Denoising and Steganography

This paper gives a summary of different works we had performed in hexagonal grid for the performance analysis of image processing in hexagonal grid. Processing based on hexagonal grid is a new approach in image processing because of its various advantages. This paper covers image compression and image denoising in hexagonal grid. Image compression using wavelet transform is performed in hexagonal grid. The performance of compression in hexagonal grid and conventional rectangular grid are analyzed in terms of Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). It is observed that better PSNR is obtained in the case of hexagonal grid. Wavelet thresholding based method is done for image denoising in hexagonal grid. The thresholding method is named as ‘Jeevkrish’ method, which is the modification of Neigh Shrink thres holding method. The performance is analyzed using MSE, PSNR and Structural Similarity (SSIM) Index. An algorithm (‘JK’ algorithm) for generating a pseudo hexagonal structure image and a method for image hiding using the pseudo hexagonal image as the cover image are also discussed in this paper.

A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts

Unmanned aerial vehicles (UAVs) equipped with compact digital cameras and multi-spectral sensors are used in remote sensing applications and environmental studies. Recently, due to the reduction of costs of these types of system, the increase in their reliability, and the possibility of image acquisition with very high spatial resolution, low altitudes imaging is used in many qualitative and quantitative analyses in remote sensing. Also, there has been an enormous development in the processing of images obtained with UAV platforms. Until now, research on UAV imaging has focused mainly on aspects of geometric and partially radiometric correction. And consideration of the effects of low atmosphere and haze on images has so far been neglected due to the low operating altitudes of UAVs. However, it proved to be the case that the path of sunlight passing through various layers of the low atmosphere causes refraction and causes incorrect registration of reflection by the imaging sensor. Images obtained from low altitudes may be degraded due to the scattering process caused by fog and weather conditions. These negative atmospheric factors cause a reduction in contrast and colour reproduction in the image, thereby reducing its radiometric quality. This paper presents a method of dehazing images acquired with UAV platforms. As part of the research, a methodology for imagery acquisition from a low altitude was introduced, and methods of atmospheric calibration based on the atmosphere scattering model were presented. Moreover, a modified dehazing model using Wiener’s adaptive filter was presented. The accuracy assessment of the proposed dehazing method was made using qualitative indices such as structural similarity (SSIM), peak signal to noise ratio (PSNR), root mean square error (RMSE), Correlation Coefficient, Universal Image Quality Index (Q index) and Entropy. The experimental results showed that using the proposed dehazing method allowed the removal of the negative impact of haze and improved image quality, based on the PSNR index, even by an average of 34% compared to other similar methods. The obtained results show that our approach allows processing of the images to remove the negative impact of the low atmosphere. Thanks to this technique, it is possible to obtain a dehazing effect on images acquired at high humidity and radiation fog. The results from this study can provide better quality images for remote sensing analysis.

Statistical multidirectional line dark channel for single‐image dehazing

Outdoor scenes often contain atmospheric degradation, such as fog or haze, which deteriorate the performance of tracking, autonomous driving and surveillance systems, among others, making dehazing methods an area of considerable interest. However, some dehazing techniques are computationally demanding, generating a trade-off between time-consumption and restoration quality. A new method is proposed for improving outdoor images taken with haze effects while making them less time-consuming. The proposed method was inspired by the Radon transform and tailored for dehazing images by computing the dark channel, in addition to using statistical computations and a heuristic approach to avoid saturated areas. The results obtained were subjected to a reduced-reference image quality dehazing assessment, a full-reference metrics Structural SIMilarity (SSIM) index and peak signal-to-noise ratio (PSNR) over real-world and synthetic outdoor images. The results demonstrate that the proposed method presents an adequate balance between new visible edges, increased gradient and saturated pixels, in addition to obtaining at least a 5% increase in the SSIM index and, a 16% increase in the PSNR index, as well as being 5.37 times faster than the four dehazing methods recently introduced in the literature.

Removing the Traces of Median Filtering via Unsharp Masking as an Anti-forensic Approach in Medical Imaging

Development of post-processing algorithms which cannot be detected by forensic tools is an active area of research in image processing. Median Filter (MF) is one among the denoising schemes which is specifically targeted by the forensic toolsbecause of its wide application in commercial raster graphic editors, simplicity, fast computation and detail preserving characteristics. Methodsbased on Convolutional Neural Networks (CNN) and Variational Deconvolution (VD), meant for reducing the forensic detectability of MF by removing the traces of filtering from the output images are computationally intense. A simple and computationally feasible approach for removing the traces of median filtering from the output images, thereby to reduce the forensic detectability of MF is proposed in this paper. In the proposed approach, blurred edges in the output of MF are restored with the help of Unsharp Masking (UM). Optimum value of the amount which controls the degree of sharpening in the UM algorithm is determined via minimum error sense criterion by making use of Peak Signal to Noise Ratio (PSNR) between input and processed images as objective function. Values of PSNR and Structural Similarity Index Metric (SSIM) between input and output images exhibited by the proposed algorithm are found to be higher than those exhibited by methods based on CNN, VD and combined framework of VD and Total Variation (TV) minimisation.

An improved capacity data hiding technique based on image interpolation

Data hiding in multimedia objects such as text, images, audio and video clips is a technique that has been widely used to achieve security for applications requiring copyright protection, covert communication, and tampering detection. Data hiding can be irreversible or reversible, where the latter is used to ensure exact recovery of the media object after extracting the hidden data. Different approaches to achieve reversible data hiding have been proposed. One of the approaches is the interpolation-based data hiding which has been proposed to achieve high data hiding capacity. This paper presents a new computationally simple interpolation-based data hiding technique that increases data hiding capacity and limits the cover image distortion that is caused by the two major steps of interpolation-based techniques; the downscaling/expansion step and the data hiding step . Image distortion reduction in the downscaling/expansion step is achieved by using a new image interpolation algorithm, whereas the image distortion in the data hiding step is reduced utilizing a new adjustable data hiding algorithm, which adaptively adjusts the level of tradeoff between data hiding capacity and image quality. The performance of the proposed technique has been evaluated for data hiding capacity and image quality using three metrics: peak signal to noise ratio (PSNR), weighted PSNR (WPSNR), and structural similarity index (SSIM). The data hiding capacity achieved by the proposed technique can be as high as 1.7bpp, which is higher by 8.5 to 72% as compared to similar techniques. Moreover, for the data hiding step, the proposed algorithm achieved high quality images as reflected by values up to 54 dB for PSNR, 78 dB for WPSNR, and 0.9998 for SSIM.

Determination of optimal parameters for bilateral filter in brain MR image denoising

Noise elimination is an important pre-processing step in magnetic resonance (MR) images for clinical purposes. In the present study, as an edge-preserving method, bilateral filter (BF) was used for Rician noise removal in MR images. The choice of BF parameters affects the performance of denoising. Therefore, as a novel approach, the parameters of BF were optimized using genetic algorithm (GA). First, the Rician noise with different variances (σ=10, 20, 30) was added to simulated T1-weighted brain MR images. To find the optimum filter parameters, GA was applied to the noisy images in searching regions of window size [3×3, 5×5, 7×7, 11×11, and 21×21], spatial sigma [0.1–10] and intensity sigma [1–60]. The peak signal-to-noise ratio (PSNR) was adjusted as fitness value for optimization.After determination of optimal parameters, we investigated the results of proposed BF parameters with both the simulated and clinical MR images. In order to understand the importance of parameter selection in BF, we compared the results of denoising with proposed parameters and other previously used BFs using the quality metrics such as mean squared error (MSE), PSNR, signal-to-noise ratio (SNR) and structural similarity index metric (SSIM). The quality of the denoised images with the proposed parameters was validated using both visual inspection and quantitative metrics. The experimental results showed that the BF with parameters proposed by us showed a better performance than BF with other previously proposed parameters in both the preservation of edges and removal of different level of Rician noise from MR images. It can be concluded that the performance of BF for denoising is highly dependent on optimal parameter selection.

Patch based image denoising using the finite ridgelet transform for less artifacts

Patch based denoising methods have proved to lead to state-of-the-art results. However, in contrast with intensive pursuing of higher peak signal to noise ratio (PSNR), less attention is paid to visual quality improvement of denoised images. In this paper, we first compare the denoising performance in edge and smooth regions. Results reveal that edge regions are the main source for potential performance improvement. This motivates us to investigate the use of the finite ridgelet transform as a local transform for better preservation of directional singularities. A two stage denoising algorithm is then proposed to improve the representation of detail structures. Experimental results in denoising images which only contain white noise show that the proposed algorithm consistently outperforms other methods in terms of PSNR and Structural SIMilarity index. Denoised images by the proposed method also demonstrate good visual quality with the least artifacts and fake structures in experiments on natural images.

Quality assessment method for geometrically distorted images

The objective assessment of image quality is important for image processing, which has been paid much attention to in recent years. However, there were few reports about objective quality assessment methods for geometrically distorted images. Different from the routine image degradation processing (for example, noise addition, contrast change and lossy compression), the geometric distortion results in the changes of the spatial relationship of image pixels, which makes the traditional quality assessment algorithms, such as mean square error (MSE) and peak signal to noise ratio (PSNR) failure to obtain expected assessment results. In this paper, a full reference image quality assessment algorithm is proposed specifically for the quality evaluation of geometrically distorted images. This assessment algorithm takes into account three key factors, such as distortion intensity, distortion change rate and line feature index for perceptual quality assessment of images. Experimental results in this study show that the proposed assessment algorithm not only is significantly better than those of the traditional objective assessment methods such as PSNR and structural similarity index measurement (SSIM), but also has significant correlation with human subjective assessment.

A dual adaptive watermarking scheme in contourlet domain for DICOM images.

BackgroundNowadays, medical imaging equipments produce digital form of medical images. In a modern health care environment, new systems such as PACS (picture archiving and communication systems), use the digital form of medical image too. The digital form of medical images has lots of advantages over its analog form such as ease in storage and transmission. Medical images in digital form must be stored in a secured environment to preserve patient privacy. It is also important to detect modifications on the image. These objectives are obtained by watermarking in medical image.MethodsIn this paper, we present a dual and oblivious (blind) watermarking scheme in the contourlet domain. Because of importance of ROI (region of interest) in interpretation by medical doctors rather than RONI (region of non-interest), we propose an adaptive dual watermarking scheme with different embedding strength in ROI and RONI. We embed watermark bits in singular value vectors of the embedded blocks within lowpass subband in contourlet domain.ResultsThe values of PSNR (peak signal-to-noise ratio) and SSIM (structural similarity measure) index of ROI for proposed DICOM (digital imaging and communications in medicine) images in this paper are respectively larger than 64 and 0.997. These values confirm that our algorithm has good transparency. Because of different embedding strength, BER (bit error rate) values of signature watermark are less than BER values of caption watermark. Our results show that watermarked images in contourlet domain have greater robustness against attacks than wavelet domain. In addition, the qualitative analysis of our method shows it has good invisibility.ConclusionsThe proposed contourlet-based watermarking algorithm in this paper uses an automatically selection for ROI and embeds the watermark in the singular values of contourlet subbands that makes the algorithm more efficient, and robust against noise attacks than other transform domains. The embedded watermark bits can be extracted without the original image, the proposed method has high PSNR and SSIM, and the watermarked image has high transparency and can still conform to the DICOM format.