scholarly journals Color Enhancement of Low Illumination Garden Landscape Images

2021 ◽  
Vol 38 (6) ◽  
pp. 1747-1754
Author(s):  
Qian Zhang ◽  
Shuang Lu ◽  
Lei Liu ◽  
Yi Liu ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Digital Image Processing ◽  
Landscape Design ◽  
Dynamic Threshold ◽  
Color Restoration ◽  
Low Illumination ◽  
Color Enhancement ◽  
Landscape Information ◽  
Image Details

The unfavorable shooting environment severely hinders the acquisition of actual landscape information in garden landscape design. Low quality, low illumination garden landscape images (GLIs) can be enhanced through advanced digital image processing. However, the current color enhancement models have poor applicability. When the environment changes, these models are easy to lose image details, and perform with a low robustness. Therefore, this paper tries to enhance the color of low illumination GLIs. Specifically, the color restoration of GLIs was realized based on modified dynamic threshold. After color correction, the low illumination GLI were restored and enhanced by a self-designed convolutional neural network (CNN). In this way, the authors achieved ideal effects of color restoration and clarity enhancement, while solving the difficulty of manual feature design in landscape design renderings. Finally, experiments were carried out to verify the feasibility and effectiveness of the proposed image color enhancement approach.

Bio-Medical Image Processing

2020 ◽  
pp. 158-169
Author(s):  
Rasmita Lenka ◽  
Koustav Dutta ◽  
Ashimananda Khandual ◽  
Soumya Ranjan Nayak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Early Detection ◽  
Digital Image Processing ◽  
Medical Science ◽  
Medical Image Processing ◽  
The World ◽  
Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

scholarly journals APLIKASI PENGENALAN PLAT NOMOR KENDARAAN DI UNIVERSITAS RIAU

2019 ◽  
Vol 9 (3) ◽  
pp. 47-52
Author(s):  
Noprizal ◽  
Feri Candra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Digital Image Processing ◽  
Digital Image ◽  
Training Data ◽  
License Plate ◽  
Backpropagation Method ◽  
Artificial Neural

Abstract Vehicle license plate recognition application has been found in shopping centers, university, and other agency buildings with various methods of recognition. Some examples of methods used such as digital image processing techniques, neural networks and so forth. This study makes an application for the introduction of license plates, especially for student vehicle license plates in the university area. This application is developed with Digital Image Processing Methods and Artificial Neural Networks. In this study, 900 training data are used, taken from 200 photo vehicle number plates, to train 36 characters that contain 26 alphabets and 10 decimal numbers. The training data is used to test 30 photos of vehicle license plates. Plate photos used as training and testing data are the Indonesian standard with black and white plates. Artificial Neural Network used to recognize vehicle license plate by using the Backpropagation method with parameters Epoch 1000, Hidden layer1 with node 60, Hidden layer2 with node 55, Goal 0.001. The final conclusion of this Study shows that the use of Artificial Neural Network Backpropagation method is very good, with the best testing accuracy obtained, namely 98% and 1.25 error. Keywords : digital image processing, artificial neural networks, vehicle license plate Abstrak Aplikasi pengenalan plat nomor kendaraan sudah banyak ditemukan di pusat perbelanjaan, universitas, dan gedung instansi dengan berbagai metode pengenalan. Beberapa contoh metode yang digunakan seperti teknik pengolahan citra digital, jaringan syaraf tiruan dan lain sebagainya. Disini penulis membuat sebuah aplikasi pengenalan plat nomor kendaraan khususnya untuk plat nomor kendaraan mahasiswa yang ada dilikungan Universitas Riau. Aplikasi ini dikembangkan dengan metode pengolahan citra digital dan jaringan syaraf tiruan. Pada penelitian ini, digunakan 700 data pelatihan yang diambil dari 200 foto plat nomor, untuk melatih 36 karakter. Data pelatihan tersebut kemudian digunakan untuk menguji 30 foto plat nomor kendaraan. Foto plat yang dijadikan untuk data pelatihan dan pengujian yaitu plat standar indonesia yang berwarna hitam dan putih. Jaringan syaraf tiruan yang digunakan untuk melakukan pengenalan yaitu dengan Metode Backpropagation dengan parameter Epoch 1000, Hidden layer1 dengan jumlah node 60, Hidden layer2 dengan jumlah node 55, Goal  0,001. Kesimpulan akhir dari penelitian ini yaitu menunjukan bahwa penggunaan Metode Backpropagation jaringan syaraf tiruan ini sangat bagus, dengan akurasi pengujian terbaik yang didapat yaitu 98% dengan eror 1,25. Kata kunci: pengolahan citra digital, jaringan syaraf tiruan, Backpropagation, plat nomor  

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