scholarly journals GLAGC: Adaptive Dual-Gamma Function for Image Illumination Perception and Correction in the Wavelet Domain

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 845
Author(s):  
Wenyong Yu ◽  
Haiming Yao ◽  
Dan Li ◽  
Gangyan Li ◽  
Hui Shi
Keyword(s):  
Discrete Wavelet ◽  
Exposure Level ◽  
Gamma Correction ◽  
Adaptive Stabilization ◽  
Low Contrast ◽  
Adaptive Correction ◽  
Low Illumination ◽  
Adaptive Gamma Correction ◽  
Entropy Criterion ◽  
The Relationship

Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform. Two features that characterize the image illuminance are creatively designed. The first feature is the spatial luminance distribution feature, which is applied to the adaptive gamma correction of local uneven lighting. The other feature is the global statistical luminance feature. Through a training set containing images with various illuminance conditions, the relationship between the image exposure level and the feature is estimated under the maximum entropy criterion. It is used to perform adaptive gamma correction on global low illumination. Moreover, smoothness preservation is performed in the high-frequency subband to preserve edge smoothness. To eliminate low-illumination noise after wavelet reconstruction, the adaptive stabilization factor is derived. Experimental results demonstrate the effectiveness of the proposed algorithm. By comparison, the proposed method yields comparable or better results than the state-of-art methods in terms of efficiency and quality.

scholarly journals Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm

2021 ◽  
Vol 38 (1) ◽  
pp. 39-50
Author(s):  
Zohair Al-Ameen
Keyword(s):  
Fuzzy Set ◽  
Quality Evaluation ◽  
Low Complexity ◽  
Correction Method ◽  
Image Feature ◽  
Type Ii ◽  
Gamma Correction ◽  
Low Contrast ◽  
High Resolution Images ◽  
Grayscale Images

Contrast is a distinctive image feature that tells if it has adequate visual quality or not. On many occasions, images are captured with low-contrast due to inevitable obstacles. Therefore, an improved type-II fuzzy set-based algorithm is developed to enhance the contrast of various color and grayscale images properly while preserving the brightness and providing natural colors. The proposed algorithm utilizes new upper and lower ranges, amended Hamacher t-conorm, and a transform-based gamma correction method to provide the enhanced images. The proposed algorithm is assessed with artificial and real contrast distorted images, compared with twelve specialized methods, and the outcomes are evaluated using four advanced metrics. From the obtained results of experiments and comparisons, the developed algorithm demonstrated the ability to process various color and grayscale images, performed the best among the comparative methods, and scored the best in all four quality evaluation metrics. The findings of this study are significant because the proposed algorithm has low-complexity and can adjust the contrast of different images expeditiously, which enables it to be used with different imaging modalities especially those with limited hardware resources or produce high-resolution images.

Infrared image detail enhancement based on local adaptive gamma correction

2012 ◽  
Vol 10 (2) ◽  
pp. 021002-21006 ◽  
Author(s):  
Bin Liu Bin Liu ◽  
Xia Wang Xia Wang ◽  
Weiqi Jin Weiqi Jin ◽  
Yan Chen Yan Chen ◽  
Chongliang Liu Chongliang Liu ◽  
...  
Keyword(s):  
Infrared Image ◽  
Gamma Correction ◽  
Adaptive Gamma Correction ◽  
Image Detail ◽  
Detail Enhancement

scholarly journals Visual Perception during Mirror-Gazing at One’s Own Face in Patients with Depression

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Giovanni B. Caputo ◽  
Marco Bortolomasi ◽  
Roberta Ferrucci ◽  
Mario Giacopuzzi ◽  
Alberto Priori ◽  
...  
Keyword(s):  
Visual Perception ◽  
Facial Expression ◽  
Facial Recognition ◽  
Face Image ◽  
Illumination Level ◽  
Healthy Controls ◽  
Self Evaluation ◽  
Low Illumination ◽  
Mirror Gazing ◽  
The Relationship

In normal observers, gazing at one’s own face in the mirror for a few minutes, at a low illumination level, produces the apparition of strange faces. Observers see distortions of their own faces, but they often see hallucinations like monsters, archetypical faces, faces of relatives and deceased, and animals. In this research, patients with depression were compared to healthy controls with respect to strange-face apparitions. The experiment was a 7-minute mirror-gazing test (MGT) under low illumination. When the MGT ended, the experimenter assessed patients and controls with a specifically designed questionnaire and interviewed them, asking them to describe strange-face apparitions. Apparitions of strange faces in the mirror were very reduced in depression patients compared to healthy controls. Depression patients compared to healthy controls showed shorter duration of apparitions; minor number of strange faces; lower self-evaluation rating of apparition strength; lower self-evaluation rating of provoked emotion. These decreases in depression may be produced by deficits of facial expression and facial recognition of emotions, which are involved in the relationship between the patient (or the patient’s ego) and his face image (or the patient’s bodily self) that is reflected in the mirror.

scholarly journals An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

2019 ◽  
Vol 9 (2) ◽  
pp. 2618-2629
Keyword(s):  
Deep Learning ◽  
Plant Diseases ◽  
Classification Model ◽  
Effective Characteristics ◽  
Gamma Correction ◽  
Financial Loss ◽  
Citrus Diseases ◽  
Otsu Method ◽  
Adaptive Gamma Correction ◽  
Citrus Disease

In farming sector, diseases affected in plants are mainly accountable for the minimized profit that leads to financial loss. In case of plants, citrus is utilized as a main resource of nutrients namely vitamin C globally. But citrus diseases greatly affect the productivity as well as quality. In recent days, computer vision and image processing approaches are commonly applied for detecting and classifying the plant diseases. This paper presents a novel deep learning (DL) based citrus disease detection and classification model. A new DL based AlexNet architecture is employed for effective identification of diseases. The presented model involves four main processes namely pre-processing, segmentation, feature extraction, and classification. Initially, pre-processing takes place to improve the quality of the image. Then, the Otsu method is applied to segment the images. Next, Alex-Net model is applied as a feature extractor. Finally, random forest (RF) classifier is used to classify the different kinds of citrus diseases. Besides, adaptive gamma correction (AGC) model is applied to improve the contrast of the applied citrus images. A comprehensive experimentation takes place on Citrus Disease Image Gallery Dataset. The results are examined under several cases and the outcome ensured the effective characteristics of the presented AGC-A model

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