scholarly journals PENERAPAN RANDOM FOREST UNTUK PENGENALAN JENIS IKAN BERDASARKAN PERBAIKAN CITRA CLAHE DAN DARK CHANNEL PRIOR

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
R.A. Pramunendar ◽  
Dwi Puji Prabowo ◽  
F. Alzami ◽  
R.A. Megantara
Keyword(s):  
Random Forest ◽  
Histogram Equalization ◽  
Dark Channel Prior ◽  
Adaptive Histogram Equalization ◽  
Dark Channel

Ancaman terhadap kekayaan alam semakin terlihat, oleh karena itu upaya untuk melindungi populasi biota perairan sangat menantang bagi banyak negara. Upaya untuk mengatasi kerusakan terhadap populasi ikan asli telah dilakukan dengan mengurangi populasi ikan invasif melalui teknik penangkapan ikan tradisional. Namun, teknik penangkapan tersebut tidak hanya menangkap spesies ikan invasif tetapi juga spesies asli. Oleh karena itu, masih diperlukan proses manual untuk memilah hasil tangkapan sehingga menghabiskan energi dan waktu. Maka, perlu ditingkatkan kemampuan pengenalan ikan secara otomatis dengan bantuan computer. Telah ada penelitian sebelumnya untuk mengenali jenis-jenis ikan, namun tidak banyak yang mempertimbangkan adanya noice atau artefak-artefak yang timbul karena kondisi bawah air serta efek fitur-fitur ikan yang saling berkaitan. Oleh karena itu dalam penelitian ini, peneliti  ini mengusulkan untuk melakukan analisis dampak pre-processing dari kombinasi algoritma CLAHE dan DCP yang diterapkan dalam klasifikasi ikan dengan Random Forest. Pre-processing yang yang diberikan bertujuan untuk mengatasi artefak atau noice yang timbul pada citra bawah air dan mengatasi efek dari fitur-fitur keragaman jenis ikan. Sehingga diharapkan mampu menghasilkan klasifikasi yang lebih baik dari penelitian sebelumnya. Klasifikasi dengan menggunakan Random Forest (RF) dengan perbaikan citra Dark Channel Prior (DCP) dan Contract Limited Adaptive Histogram Equalization (CLAHE), terbukti memberikan nilai akurasi rata-rata yang cukup tinggi yakni sebesar 98.51%, presisi 78.91%, dan recall 36.71%.

scholarly journals Image Defogging Framework Using Segmentation and the Dark Channel Prior

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 285
Author(s):  
Sabiha Anan ◽  
Mohammad Ibrahim Khan ◽  
Mir Md Saki Kowsar ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Homogeneous Region ◽  
Experimental Result ◽  
Dark Channel Prior ◽  
Color Information ◽  
Visual Effects ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Processing Step ◽  
Dark Channel

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

Research on Images Restoration Method under the Foggy Environment

2014 ◽  
Vol 1070-1072 ◽  
pp. 2037-2040
Author(s):  
Hui Liu ◽  
Xue Bin Liu
Keyword(s):  
Histogram Equalization ◽  
Paper Analysis ◽  
Dark Channel Prior ◽  
Atmospheric Scattering ◽  
Adaptive Histogram Equalization ◽  
Dark Channel ◽  
Histogram Similarity ◽  
Restoration Method ◽  
Degraded Images

Because of the atmospheric scattering phenomenon, weather atmospheric degraded images captured in foggy environment have poor contrast and visibility, it has seriously affected the quality of the images. So this paper analysis and find something different between the dark channel prior and the interpolating self-adaptive histogram equalization method based on physical and non-physical model. And using the histogram similarity evaluation is evaluated on them. Finally, further discussion are indicated on techniques challenges and future development.

scholarly journals An Enhancement of Underwater Images using DCP and CLAHE Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 2805-2813
Keyword(s):  
Image Enhancement ◽  
Correlation Method ◽  
Histogram Equalization ◽  
Experimental Result ◽  
Dark Channel Prior ◽  
Resource Requirement ◽  
Adaptive Histogram Equalization ◽  
Underwater Image ◽  
Dark Channel ◽  
Rgb Image

The lack of resource requirement in this population world, we are in a position to require another resources. In this regard, ocean is one of our sustenance. It is the exact platform for various applications like, transport, food, energy etc., but still we are surveyed partly at all aspects. One of the main focus of challenge is scattering of light as it penetrate from air to water which presents us with a bluish background while studying the scenery. In this, added to this there is a hazy appearance in the visuals and calls for Image Enhancement techniques. Here, Dark Channel Prior(DCP) is used to remove the haze and noise induced by the bluish environment. However, this proposal of method is also used to increase darkness of the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used on the RGB image to enhance the contrast and intensity of the image. Finally, we get visually pleasing result, colour correlation method is carried out. The experimental result shows that a enhanced underwater image from the base image, and mostly useful to analyze and monitoring the underwater images.

scholarly journals Dehazing Effects on Image and Video using AHE, CLAHE and Dark Channel Prior

2020 ◽  
Vol 9 (3) ◽  
pp. 119-125
Keyword(s):  
Inverse Problem ◽  
Histogram Equalization ◽  
Dark Channel Prior ◽  
Underwater Imaging ◽  
Satellite Imaging ◽  
Adaptive Histogram Equalization ◽  
Autonomous Cars ◽  
Dark Channel ◽  
Ill Posed ◽  
Physical Degradation

The image captured by camera is degraded by various atmospheric parameters for example rain, storm, wind, haze, snow. The removing haze is called dehazing, is naturally done in the physical degradation model that requires a resolution of an ill-posed inverse problem. In this paper discussion and e relative study of Adaptive Histogram Equalization (AHE) as well as Contrast limited adaptive histogram equalization (CLAHE) and dark channel prior (DCP). This article suggest image and video dehazing technique working on DCP method. The DCP is resulted from the characteristics of images taken in outdoor that the value of intensity inside the local window is nearly equal to zero. The DCP system has good haze elimination and color managing potential for the images with various angles of haze. The dehazing is done using following four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This solution of four step DCP will give solution to ill-posed inverse problem. This dehazing techniques can be used in advanced driverless assisted systems in autonomous cars, satellite imaging, underwater imaging etc

Fog removal in images using improved dark channel prior and contrast limited adaptive histogram equalization

2019 ◽  
Vol 78 (16) ◽  
pp. 23281-23307 ◽  
Author(s):  
Rajiv Kapoor ◽  
Rashmi Gupta ◽  
Le Hoang Son ◽  
Raghvendra Kumar ◽  
Sudan Jha
Keyword(s):  
Histogram Equalization ◽  
Dark Channel Prior ◽  
Adaptive Histogram Equalization ◽  
Dark Channel

scholarly journals Restoration and Enhancement of Underwater Images Based on Bright Channel Prior

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yakun Gao ◽  
Haibin Li ◽  
Shuhuan Wen
Keyword(s):  
Histogram Equalization ◽  
New Method ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Underwater Environment ◽  
Dark Channel ◽  
Bright Channel Prior ◽  
Channel Image ◽  
Atmospheric Light

This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method could enhance the quality of underwater images effectively.

Fog Removal Algorithms for Real-Time Video Footage in Smart Cities for Safe Driving

2019 ◽  
pp. 55-86
Author(s):  
Neetu Sood ◽  
Indu Saini ◽  
Tarannum Awasthi ◽  
Milin Kaur Saini ◽  
Parul Bhoriwal ◽  
...  
Keyword(s):  
Time Complexity ◽  
Smart Cities ◽  
Histogram Equalization ◽  
Dark Channel Prior ◽  
Safe Driving ◽  
Video Footage ◽  
Video Frames ◽  
Dark Channel ◽  
Retinex Algorithm ◽  
Before And After

In this chapter, different approaches are presented for removal of fog from video footage taken in moving cars. The methodology uses different approaches, namely dark channel prior, contrast limited adaptive histogram equalization (CLAHE), the combination of two approaches (dark channel prior and CLAHE), and RETINEX algorithm combined with DWT. The algorithms are implemented in MATLAB R2015a. Moreover, the algorithms are compared based on their computational complexity and a visibility metric which is used for computing the CNR of video frames before and after the application of the algorithm. The chapter discusses which algorithm would provide better performance during night fog and daylight fog. Finally, the safe speed of the driver is calculated based on the time complexity of the algorithm used.

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