Second Derivative Like Measure Of Enhancement Research Articles

Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising

ABSTRACT Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor coupled with a Colour Filter Array (CFA). Furthermore, the demosaicking procedure is required to obtain a full-colour image. Image denoising and image demosaicking are the two important image restoration techniques, which have increased popularity in recent years. Finding a suitable strategy for multiple image restoration is critical for researchers. Hence, a deep learning (DL) based image denoising and image demosaicking is developed in this research. Moreover, the Autoregressive Circle Wave Optimization (ACWO) based Demosaicking Convolutional Neural Network (DMCNN) is designed for image demosaicking. The Quantum Wavelet Transform (QWT) is used in the image denoising process. Similarly, Quantum Wavelet Transform (QWT) is used to analyse the abrupt changes in the input image with noise. The transformed image is then subjected to a thresholding technique, which determines an appropriate threshold range. Once the threshold range has been determined, soft thresholding is applied to the resulting wavelet coefficients. After that, the extraction and reconstruction of the original image is carried out using the Inverse Quantum Wavelet Transform (IQWT). Finally, the fused image is created by combining the results of both processes using a weighted average. The denoised and demosaicked images are combined using the weighted average technique. Furthermore, the proposed QWT+DMCNN-ACWO model provided the ideal values of Peak signal-to-noise ratio (PSNR), Second derivative like measure of enhancement (SDME), Structural Similarity Index (SSIM), Figure of Merit (FOM) of 0.890, and computational time of 49.549 dB, 59.53 dB, 0.963, 0.890, and 0.571, respectively.

Deep Residual Network and Wavelet Transform-Based Non-Local Means Filter for Denoising Low-Dose Computed Tomography

Image denoising helps to strengthen the image statistics and the image processing scenario. Because of the inherent physical difficulties of various recording technologies, images are prone to the emergence of some noise during image acquisition. In the existing methods, poor illumination and atmospheric conditions affect the overall performance. To solve these issues, in this paper Political Taylor-Anti Coronavirus Optimization (Political Taylor-ACVO) algorithm is developed by integrating the features of Political Optimizer (PO) with Taylor series and Anti Coronavirus Optimization (ACVO). The input medical image is subjected to noisy pixel identification step, in which the deep residual network (DRN) is used to discover noise values and then pixel restoration process is performed by the created Political Taylor-ACVO algorithm. Thereafter image enhancement mechanism strategy is done using vectorial total variation (VTV) norm. On the other hand, original image is applied to discrete wavelet transform (DWT) such that transformed result is fed to non-local means (NLM) filter. An inverse discrete wavelet transform (IDWT) is utilized to the filtered outcome for generating the denoised image. Finally, image enhancement result is fused with denoised image computed through filtering model to compute fused output image. The proposed model observed the value for Peak signal-to-noise ratio (PSNR) of 29.167 dB, Second Derivative like Measure of Enhancement (SDME) of 41.02 dB, and Structural Similarity Index (SSIM) of 0.880 for Gaussian noise.

Optimal Deep CNN-Based Vectorial Variation Filter for Medical Image Denoising.

Medical imaging has acquired more attention due to the emerging design of wireless technologies, the internet, and data storage. The reflection of these technologies has gained attraction in medicine and medical sciences facilitating the diagnosis and treatment of different diseases in an effective manner. However, medical images are vulnerable to noise, which can make the image unclear and perplex the identification. Thus, denoising of medical images is imperative for processing medical images. This paper devises a novel optimal deep convolution neural network-based vectorial variation (ODVV) filter for denoising medical computed tomography (CT) images and Lena images. Here, the input medical images are fed to a noisy pixel map identification module wherein the deep convolutional neural network (Deep CNN) is adapted for discovering noisy pixel maps. Here, Deep CNN training is done with the Adam algorithm. Once noisy pixels are identified, it is further given to noise removal module which is performed using the proposed optimization algorithm, namely Feedback Artificial Lion (FAL). Here, the FAL is devised by combining the FAT and Lion algorithm. After noise removal, the pixel enhancement is performed using the vectorial total variation norm to get final pixel-enhanced image. The proposed FAL algorithm offered enhanced performance in contrast to other techniques with the highest peak signal-to-noise ratio (PSNR) of 24.149dB, highest second-derivative-like measure of enhancement (SDME) of 32.142dB, highest structural index similarity (SSIM) of 0.800, and Edge Preserve Index (EPI) of 0.9267.

Flower pollination student psychology optimization-integrated context deep learning and probabilistic-based fusion for image inpainting

Image inpainting is the process to fill missing pixels in the damaged image and this process has drawn more attraction and gained active and expensive research topic in recent decades, because the high quality in the image inpainting benefits a greater range of applications, like object removal, photo restoration, and so on. Inpainting of larger quality of the image needs to fill the empty regions with plausible content in the damaged image. The existing inpainting methods either fill image regions by stealing the image patches or semantically create coherent patches from the regional context. Most of the traditional models perform well on small holes images, but restoring the image with large holes still results a challenging task. To overcome such issues and to generate effective inpainting results, a proposed method named the hybrid context deep learning approach is designed in order to fill empty regions of crack images. Moreover, the proposed method is more effective by employing a hybrid optimization algorithm for training of classifier to generate a more robust and accurate inpainted result. The developed model includes two different deep learning classifiers to accomplish the process of image inpainting in such a way that the results are fused through the probabilistic model. Moreover, the proposed approach attains higher performance by the metrics such as Peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM), Second Derivative like Measure of Enhancement (SDME), and Universal Quality Index (UQI) with the values of 38.02[Formula: see text]db, 0.867, 54.32[Formula: see text]db, and 0.864, respectively.

Infrared Image Enhancement Based on Optimally Weighted Multi-Scale Laplacian of Gaussian and Local Statistics Using Particle Swarm Optimization

Infrared imagery is extensively used in defense, remote sensing and medical applications. While the infrared images have many advantages over RGB images, the details in these images are usually blurred which in turn leads to some difficulties for human operators. In this paper, a new method based on Laplacian of Gaussian scale-space and local variance is presented to improve the visual quality of the infrared images. At the first step, the Gaussian scale-space is constructed by convolving the original image with different Gaussian kernels. Then, the two-dimensional Laplacian kernels are convolved with the Gaussian scale-space to achieve details with both positive as well as negative contrasts. The weighted details are added to the original image to deblur the dim areas. At the final step, to increase the dynamic range of the image and have better visual quality, the local variance of the image is also added to the output of the previous step. Since finding optimum weighting coefficients is a difficult task empirically, here, we use a population-based meta-heuristic optimization algorithm called particle swarm optimization (PSO) to find the optimum values for weighting coefficient values. Beside qualitative comparison, Structural Similarity (SSIM) and second-derivative-like measure of enhancement (SDME) are used to quantitatively investigate the images quality. The proposed method outperforms the baseline algorithms in both qualitative and quantitative perspectives.

A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique

Improving the clarity and visual quality of Musculoskeletal Ultrasound Images (MUI) can help clinicians to detect diseases more easily and accurately. In this work, we described how to enhance the contrast of MUI locally based on a fuzzy inference system. Local Fuzzy Inference Technique (LFIT) was introduced as a novel technique to enhance the contrast of MUI. The input data used musculoskeletal ultrasound images were collected from healthy volunteers. Local Fuzzy Inference Technique (LFIT) was compared with a recent fuzzy technique of the image enhancement and validated based on assessment metrics (second-derivative-like measure of enhancement (SDME)). The results advocated an improved quality of the musculoskeletal ultrasound images based on the LFIT technique with approximately 11% greater than recent technique and computation time of LFIT is 28.4% is less. It is possible to apply a proposal technique on the other types of image (panoramic image and video). Furthermore, observed improvements on the MUI quality could potentially invested as a pre-processing step before performing other computer vision applications, such as image segmentation, tracking, and 3D image reconstruction.

Ant Cuckoo Search Optimization‐based deep learning classifier for image enhancement in spinal cord images

AbstractImage enhancement has been paid great attention in several areas, and various image enhancement approaches are introduced by the researchers to enhance the images clearer from the degraded images. Accordingly, this paper presents the proposed ACSO‐based IE‐CGAN for image enhancement. Initially, an effective noisy pixel identification based on image enhancement is developed, which employs circular‐based searching for improving the input image from several noises, like salt and pepper noise, speckle (impulse) noise, Gaussian noise, and random noise. Once the noisy pixels are identified, the pixel enhancement is done using the circular‐based search. The noise removal is done using a statistical model, where the best threshold value is determining using the proposed Ant Cuckoo Search Optimization (ACSO) algorithm. The ACSO algorithm is newly designed by integrating Ant Lion Optimization (ALO) and the Cuckoo Search Optimization (CSO) algorithm. After that, the contrast enhancement is carried out using Image Enhancement Conditional Generative Adversarial Network (IE‐CGAN), which is trained by the proposed ASCO algorithm. The experimentation is carried out using an osteoporotic vertebral fracture dataset, and the performance of image enhancement using ACSO‐based IE‐CGAN is evaluated based on Peak Signal to Noise ratio (PSNR), Structural Similarity Index (SSIM), and Second Derivative like Measure of Enhancement (SDME). The developed method achieves the maximal PSNR of 33.50 dB with salt and pepper noise, maximal SDME 41.38 dB with random noise, and maximal SSIM of 0.967 with salt and pepper noise.

S-DolLion-MSVNN: A Hybrid Model for Developing the Super-Resolution Image From the Multispectral Satellite Image

Abstract Super-resolution offers a new image with high resolution from the low-resolution (LR) image that is highly employed for the numerous remote sensing applications. Most of the existing techniques for formation of the super-resolution image exhibit the loss of quality and deviation from the original multi-spectral LR image. Thus, this paper aims at proposing an efficient super-resolution method using the hybrid model. The hybrid model is developed using the support vector regression model and multi-support vector neural network (MSVNN), and the weights of the MSVNN is tuned optimally using the proposed algorithm. The proposed DolLion algorithm is the integration of the dolphin echolocation algorithm and lion optimization algorithm that exhibits better convergence and offers a global optimal solution. The experimentation is performed using the datasets taken from the multi-spectral scene images. The optimal and effective formation of the super-resolution image using the proposed hybrid model outperforms the existing methods, and the analysis using the second-derivative-like measure of enhancement (SDME) ensures that the proposed method is better and yields a maximum SDME of 67.6755 dB.

Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising

Abstract Satellite image denoising is a recent trend in image processing, but faces many challenges due to the environmental factors. Previous works have developed many filters for denoising the hyperspectral satellite images. Accordingly, this work utilizes an adaptive filter with the type 2 fuzzy system and the optimization-based kernel interpolation for the satellite image denoising. Here, the image denoising has been done through three steps, namely noise identification, noise correction and image enhancement. Initially, the type 2 fuzzy system identifies the noisy pixels in the satellite image and converts the image into a binary image, which is passed through the adaptive nonlocal mean filter (ANLMF) for the noise correction. Finally, the kernel-based interpolation scheme carries out the image enhancement, which is done through the proposed chronological Jaya optimization algorithm (chronological JOA) that is developed by modifying Jaya optimization algorithm (JOA) with the chronological idea. The performance of the proposed denoising scheme is analyzed by considering the satellite images from two standard databases, namely Indian pines database and NRSC/ISRO satellite database. Also, the comparative analysis is performed with the state-of-the-art denoising methods using the evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index (SSIM) and second derivative-like measure of enhancement (SDME). From the results, it is exposed that the proposed adaptive filter with the chronological JOA has the improved performance with the PSNR of 22.0408 dB, SDME of 244.133 dB and SSIM of 0.872.

T2FCS filter: Type 2 fuzzy and cuckoo search-based filter design for image restoration

A novel filter design for the restoration of the corrupted digital image is proposed in this paper. The filter design incorporates type II fuzzy system and cuckoo search optimization algorithm (T2FCS) based filter design for the restoration of the noise in the images. The noisy pixels in the images are detected using the proposed circular based searching scheme and the detected corrupt pixels are removed using the cuckoo search algorithm. The enhanced pixels in place of the corrupt pixels are acquired using the proposed type II fuzzy system. The proposed filter adapts to various noisy conditions such as random noise, salt and pepper noise and scratch noise. The experimentation of the proposed filter design is carried out over two images. The performance of the proposed T2FCS filter design is compared over the existing image restoration algorithms using metrics; Peak Signal to Noise ratio (PSNR), Structural Similarity Index (SSIM), Second Derivative Like Measure of Enhancement (SDME). The result obtained favours the performance of the proposed filter in the restoration of the noisy images.

An efficient multi balanced cuckoo search K-means technique for segmentation and compression of compound images

The images comprise not only photographic images but also graphic and text images, they are determined in magazines, brochures and websites. The segmentation and compression of compound images (for instance, computer-generated images, scanned documents and so on) are tough to the procedure.The existing segmentation and compression techniques do not provide a complete comprehensive solution. To solve the problems in existing techniques, here we segmented the compound images via an optimization depended on K-means clustering technique along with AC (Alternate Current) coefficient method for the dynamic segmentation and then compressed individually. The AC coefficient based segmentation method results in detachment of smooth (background) and non-smooth (text, image and overlapping) areas. Further, the non-smooth part is segmented via the optimization depended on K-means clustering technique. Also, the density of segmented objects is headed applying different compression strategies such as the Huffman coder, arithmetic coder, and Jpeg coders. With the being approaches, the entire projected architecture is implemented in MATLAB and the function of the scheme is measured and equated. Our proposed system achieves better compression ratio (21.16), and also improves the performance for image quality index (0.931574), PSNR (Peak Signal to Noise Ratio) (34.91338), RMSE (Root Mean Square Error) (0.931574), SSIM (Structural Similarity) (0.546882), and SDME (Second Derivative-like Measure of Enhancement) (44.91293) than the available CS K-means algorithm.

Towards nonuniform illumination face enhancement via adaptive contrast stretching

A face enhancement has the potential to play an important part in providing satisfactory and vast information to the face recognition performance. Therefore, a new approach for nonuniform illumination face enhancements (NIFE) was proposed by designing an adaptive contrast-stretching (ACS) filter. In a more objective manner of achieving this, an investigation usage of CS function with adjustable factors value to summarise its influence on the NIFE is examined firstly. Secondly, describe a new strategy to cater for CS adaptive factors prediction using training and testing phases. A dispersion versus location (DL) descriptor was examined in the training phase to generate the faces feature vectors. Subsequently, a frame differencing module (FDM) was developed for faces label generations. In the testing phase, the approach was examined to recognise the DL descriptor and predict face label based vocabulary tree model (VTM). Thirdly, the VTM performance was examined by referring to the area under curve (AUC) score from the receiver operating characteristic (ROC). The face quality measurement was evaluated via blind reference based statistical measures (BR-SM), blind reference based DL-descriptors (BR-DL) and visual interpretation of the resulting images. The BR-SM performed through calculating the EME (Measure of Enhancement), EEME (Measure of Enhancement by Entropy), SDME (Second Derivative like Measure of Enhancement), SHP (Coefficient of Sharpness) and CPP (Contrast per Pixel). In addition, by using DL scatter, the BR-DL handles the specific relationship with regards to the local contrast to local brightness within the resulting face images. Four face image databases, namely Extended Yale B, Mobio, Feret and CMU-PIE were used. The final results attained prove that compared to the state-of-the-art methods, the proposed ACS filter implementation is the most excellent choice in terms of contrast and nonuniform illumination adjustment as well as providing images of satisfactory quality. In short, the benefits attained proves that ACS is driven with a profitable enhancement rate in providing tremendous detail concerning face recognition systems.

Facial Expression Synthesis Images Using Hybrid Neural Network with Particle Swarm Optimization Techniques

In the advance life trend, the facial expression is the visual facial outer structure of the human affective state, intellectual action, and human interchanges and facial expression go about as the key role in the movement of communication. In this paper, the facial expression synthesis performance is done using different facial expressions such as angry, sad, smile, surprise and cry of various peoples. In the proposed method, two procedures are used namely hybrid neural network (HNN) and particle swarm optimisation (PSO) algorithm. By training particle swarm optimisation and hybrid neural network, we take the desired output. In the result section, various evaluation metrics namely peak signal to noise ratio (PSNR), mean square error (MSE) and a second-derivative-like measure of enhancement (SDME) value is calculated using diverse algorithms. In this evaluation performance, the particle swarm optimisation is given enhanced output while comparing it with other techniques and the existing methods of facial expression.

An efficient microcalcifications detection based on dual spatial/spectral processing

Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparison with their surrounding tissue. Spatial non linear enhancement can be applied for microcalcification detection. However, efficiency of a such approach depends on breast density: in case of extreme breast density, the contrast between microcalcification's details and their surrounding tissue is attenuated leading to a limitation of spatially based approaches. In that case, frequency analysis such as wavelet based analysis can be more relevant for dissociating microcalcifications. The main goal of Computer Aided Detection systems (CAD) is to detect breast cancer at an early stage for all breast density classes by using entropies to enhance and then detect microcalcification details. Accordingly, we combine our approach a spatial Automatic Non Linear Stretching (ANLS) and Shannon Entropy based Wavelet Coefficient Thresholding (SE_WCT). Validation of the proposed approach is done on the Mammographic Image Analysis Society (MIAS) database. The evaluation of the contrast is based on the Second-Derivative-Like measure of enhancement(SDME). Accordingly, it yields to a mean SDME of 78.8dB on the whole database. The performance metric for evaluating our proposed CAD is the Receiver Operating Characteristic(ROC) curve and the free-response ROC (FROC). An area under the ROC curve Az = 0.92 is obtained as well as 97.14 % of True Positives (TP) with 0,48 False positives per image (FP).

SDME Quality Measure based Stopping Criteria for Iterative Deblurring Algorithms

Deblurring from motion problem with or without noise is ill-posed inverse problem and almost all inverse problem require some sort of parameter selection. Quality of restored image in iterative motion deblurring is dependent on optimal stopping point or regularization parameter selection. At optimal point reconstructed image is best matched to original image and for other points either data mismatch occurs and over smoothing is resulted. The methods used for optimal parameter selection are formulated based on correct estimation of noise variance or with restrictive assumption on noise. Some methods involved heavy computation and produce delay in final output. In this paper we propose the method which calculate visual image quality of reconstructed image with the help of Second derivative like measure of enhancement (SDME) and helps to efficiently decide optimal stopping condition which has been checked for leading image deblurring algorithm. It do not require any estimation of noise variance or no heavy computation are needed. Simulation has been done for various images including standard images for different degradation and noise condition. For test leading deblurring algoritham of Blind and Semi-Blind deblurring of Natural Images using Alternate direction method of minimizer (ADMM) is considered. The obtained results for synthetically blurred images are good even under noisy condition with ? ISNR average values 0.2914 dB. The proposed whiteness measures seek powerful solution to iterative deblurring algorithms in deciding automatic stopping criteria.

Evaluation of Perceptual Contrast and Sharpness Measures for Meteorological Satellite Images

Sharpness and contrast have great impact on perceived quality of an image. This paper focuses on sharpness and contrast measures to evaluate quality of Thermal Infrared (TIR1) channel of Indian National Satellite-3D (INSAT-3D) without using any reference image. Most of the sharpness metrics can scarcely manage to discern image quality degradation against high frequency behavior due to noise. Six Image Quality Measures (IQMs) are employed to study their behavior in terms of blur, noise and intensity changes simultaneously. Results show that (1) change in value of Measure Of Enhancement By Entropy (EMEE) is more discernible with change in contrast of an INSAT-3D image as compared to other measures and (2) Second Derivative Like Measure Of Enhancement (SDME) has a potential to distinguish high frequency content due to sharpness arisen due to un estimated noise up to some remarkable level in case of TIR1 INSAT-3D satellite images. Performance comparison of six measures against blur, noise, contrast and sharpness changes is presented.

3D-image restoration technique using Genetic Algorithm to solve blurring problems of images

Most images may not be sharp and clear due to various reasons like noise interference and is said to be in a blurred condition. Image de-blurring is fundamental in making pictures sharp and useful. Normally, along with the input blurred image, Point Spread Function (PSF) of the original image is required for the process of restoration and de-blurring. In this paper, we introduce a technique for image restoration by Richardson–Lucy algorithm where the optimised PSF is generated by the use of Genetic Algorithm (GA). Use of optimised PSF ensures that our proposed technique does not need the original image for the de-blurring purpose and can be greatly beneficial in the real time scenario cases. The dataset used for the evaluation of the proposed technique are real 3D images and the evaluation metrics used are peak signal-to-noise ratio (PSNR), Second-Derivative like Measure of Enhancement (SDME) and mean squared error (MSE). The technique is compared with existing techniques such as de-convolution method, regularisation filter, Wiener filter and Richardson–Lucy algorithm. From the results, we can observe that our proposed technique has achieved higher PSNR and SDME values and lower MSE values when compared with other techniques. We have achieved average PSNR of 70·94, SDME of 71·46 and MSE of 0·0063. The values obtained show the superior performance of the proposed technique.