What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Joonki Paik focuses on Artificial intelligence, Computer vision, Image processing, Image restoration and Edge detection.
Joonki Paik regularly ties together related areas like Signal in his Artificial intelligence studies.
Computer vision is often connected to Adaptive filter in his work.
His work in Image processing addresses subjects such as Artifact, which are connected to disciplines such as Smoothness and Blocking.
His Image restoration study combines topics in areas such as Hopfield network, Upper and lower bounds, Point spread function and Mathematical optimization.
His Edge detection research is multidisciplinary, incorporating elements of Smoothing, Triangle mesh, Surface and Interpolation.
His most cited work include:
- Recent advances in visual and infrared face recognition: a review (528 citations)
- Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering (317 citations)
- Image restoration using a modified Hopfield network (210 citations)
What are the main themes of his work throughout his whole career to date?
Joonki Paik mainly investigates Artificial intelligence, Computer vision, Image restoration, Image processing and Image.
His studies link Pattern recognition with Artificial intelligence.
His study involves Motion estimation, Filter, Tracking, Histogram and Pixel, a branch of Computer vision.
He focuses mostly in the field of Image restoration, narrowing it down to topics relating to Algorithm and, in certain cases, Mathematical optimization.
Joonki Paik combines topics linked to Optics with his work on Image processing.
His Image study deals with Distortion intersecting with Brightness.
He most often published in these fields:
- Artificial intelligence (79.97%)
- Computer vision (71.34%)
- Image restoration (22.64%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (79.97%)
- Computer vision (71.34%)
- Pattern recognition (12.54%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Image and Distortion.
As a member of one scientific family, he mostly works in the field of Artificial intelligence, focusing on Brightness and, on occasion, Color space.
In the subject of general Pattern recognition, his work in Convolutional neural network and Sparse approximation is often linked to Context, thereby combining diverse domains of study.
Joonki Paik has included themes like Basis, Wavelet transform and Dual in his Image study.
Joonki Paik has researched Distortion in several fields, including Transmission and Image restoration.
His Image restoration research incorporates elements of Point spread function and Image fusion.
Between 2015 and 2021, his most popular works were:
- Low-light image enhancement using variational optimization-based Retinex model (55 citations)
- Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement (43 citations)
- Low-light image enhancement using variational optimization-based retinex model (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of investigation include Artificial intelligence, Computer vision, Image processing, Image and Pattern recognition.
Artificial intelligence connects with themes related to Distortion in his study.
His works in Image restoration, Feature extraction, Histogram, Optical flow and Video tracking are all subjects of inquiry into Computer vision.
The concepts of his Image restoration study are interwoven with issues in Image resolution, Zoom, Dual, Image warping and Histogram equalization.
His work carried out in the field of Image processing brings together such families of science as Image quality and Optics.
His Sparse approximation study, which is part of a larger body of work in Pattern recognition, is frequently linked to Context, bridging the gap between disciplines.
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