An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition

Vishwakarma, Virendra P.; Goel, Tripti

doi:10.1007/s11042-018-6837-0

Cited by 18 publications

(5 citation statements)

References 41 publications

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“…It reveals the data as a total of cosine functions fluctuating at separate frequencies [ 55 ]. Usually, the DCT is applied to the data to get the DCT coefficients which are split into two groups [ 56 , 57 ]; low frequencies are known as DC coefficients, and high frequencies are known as AC coefficients. High frequencies illustrate edge, details, and tiny changes [ 57 ], while low frequencies are linked with the brightness situations.…”

Section: Methodsmentioning

confidence: 99%

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Attallah

2021

Diagnostics

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Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

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Section: Methodsmentioning

confidence: 99%

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Attallah

2021

Diagnostics

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“…In another simple method [39], the authors combined CLAHE with different filters in order to enhance the image contrast and also to reduce the noise. Vishwakarma and Goel [40] used CLAHE along with logarithm transform for image contrast enhancement. Palanisamy et al [42] proposed a hybrid approach for image contrast enhancement that combines singular value equalization, gamma correction and CLAHE.…”

Section: Methodsmentioning

confidence: 99%

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

et al. 2019

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Contrast enhancement is important and plays vital role in many applications. Histogram equalization-based techniques are widely used techniques for contrast enhancement. However, it faces the contrast over-stretching, which in return causes the loss of details and unnatural look to the target image. To address this issue, this work presents a novel scheme for image contrast enhancement. The contribution of the proposed scheme is twofold. First, the image can lose many important information when an image size is decreased. For that, the image is transformed from spatial to wavelet domain so that the multi-resolution can be achieved. Second, Gamma correction is a proven technique that produces natural look and preserves mean brightness of an image with the choice of optimal gamma values. Here, Particle Swarm Optimization (PSO) is utilized to select the optimal gamma values. In this study, an effective fitness function is proposed to maximize the performance of PSO. Experimental findings show that the proposed approach improve the image contrast up to a greater extent without introducing any artifacts.

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“…The CLAHE worked on limiting the contrast and thus not suitable for natural images. For enhancement in contrast, Vishwakarma et al [23] used the combination of CLAHE and logarithmic function. Even, the limitation of CLAHE is monitored by logarithmic function but logarithmic has the property to over-enhance the image by mapping the intensity values to the higher portion of values.…”

Section: Qualitative Measure Of Kodak Datasetmentioning

confidence: 99%

Gamma Correction for Brightness Preservation in Natural Images

Rekhi¹,

Sidhu²,

Arora³

2023

Computer Systems Science and Engineering

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Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D-discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning the scale value. The scale is the modified constant value used in the logarithmic function. Further, the scale value is optimized to obtain better visual quality in the image. The optimized value is the weighted distribution of standard deviation-mean of low pass coefficients. Finally, the experimental result is estimated in terms of quality assessment measures used as absolute mean brightness error, the measure of information detail, signal to noise ratio and patch-based contrast quality in the image. By comparison, the proposed method proved to be suitably remarkable in retaining the mean brightness and better visual quality of the image.

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An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition

Cited by 18 publications

References 41 publications

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

Gamma Correction for Brightness Preservation in Natural Images

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