Color image enhancement of low-resolution images captured in extreme lighting conditions

Krieger, Evan; Asari, Vijayan K.; Arigela, Saibabu

doi:10.1117/12.2050716

“…LTISN [41] is a nonlinear and pixel by pixel approach, where the improved intensity values are calculated by applying the inverse sine function with a tunable parameter based on the nearby pixel values given in the equations ( 4), ( 5), ( 6), ( 7) and ( 8). The intensity range of the image is rescaled to [0 1] followed by a nonlinear transfer function.…”

Section: Locally Tuned Inverse Sine Nonlinear (Ltisn)mentioning

confidence: 99%

Computer Science & Information Technology (CS & IT)

2021

1

0

View full text Add to dashboard Cite

In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%

show abstract

“…LTISN [41] is a nonlinear and pixel by pixel approach, where the improved intensity values are calculated by applying the inverse sine function with a tunable parameter based on the nearby pixel values given in the equations ( 4), ( 5), ( 6), ( 7) and (8). The intensity range of the image is rescaled to [0 1] followed by a nonlinear transfer function.…”

Section: Locally Tuned Inverse Sine Nonlinear (Ltisn)mentioning

confidence: 99%

Untitled

2021

IJAIA

0

View full text Add to dashboard Cite

In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%

show abstract

“…The self-tunable transformation function is an improved version of the LTSN that uses an inverse sine function as it nonlinear function [10][11]. The function that is used for the STTF is ( , ) = sin ( ( , ) ).…”

Section: Intensity Enhancementmentioning

confidence: 99%

“…Instead, a single frame super resolution technique is used that achieves the resolution increase using only the information from one image. The super resolution process that is used in the proposed technique is as seen in [11,14] using a machine learning approach with kernel regression. The super resolution method is a single frame super resolution technique that does not rely on temporal information.…”

Section: Spatial Resolution Enhancementmentioning

confidence: 99%

Intensity and resolution enhancement of local regions for object detection and tracking in wide area surveillance

Krieger

¹

,

Asari

²

,

Arigela

³

et al. 2015

SPIE Proceedings

Self Cite

0

View full text Add to dashboard Cite

Object tracking in wide area motion imagery is a complex problem that consists of object detection and target tracking over time. This challenge can be solved by human analysts who naturally have the ability to keep track of an object in a scene. A computer vision solution for object tracking has the potential to be a much faster and efficient solution. However, a computer vision solution faces certain challenges that do not affect a human analyst. To overcome these challenges, a tracking process is proposed that is inspired by the known advantages of a human analyst. First, the focus of a human analyst is emulated by doing processing only the local object search area. Second, it is proposed that an intensity enhancement process should be done on the local area to allow features to be detected in poor lighting conditions. This simulates the ability of the human eye to discern objects in complex lighting conditions. Third, it is proposed that the spatial resolution of the local search area is increased to extract better features and provide more accurate feature matching. A quantitative evaluation is performed to show tracking improvement using the proposed method. The three databases, each grayscale sequences that were obtained from aircrafts, used for these evaluations include the Columbus Large Image Format database, the Large Area Image Recorder database, and the Sussex database.

show abstract

Color image enhancement of low-resolution images captured in extreme lighting conditions

Cited by 7 publications

References 21 publications

Computer Science & Information Technology (CS & IT)

Computer Science & Information Technology (CS & IT)

Untitled

Intensity and resolution enhancement of local regions for object detection and tracking in wide area surveillance

Contact Info

Product

Resources

About