Dehazing Citra Kabut Gunung Berapi Kelud Dengan Color Attenuation Prior Dan Adaptive Gamma Correction

Oddy Virgantara Putra, Aziz Musthafa

Abstract

Abstrak

Visibilitas citra luar ruangan yang ditangkap dalam cuaca buruk seringkali menurun karena adanya kabut, badai pasir, dan sebagainya. Visibilitas yang buruk, yang disebabkan oleh fenomena di atmosfer, menjadi faktor penyebab gagalnya aplikasi visi komputer, seperti sistem pengenalan objek luar, sistem deteksi rintangan atau sistem pengawasan video. Sejak letusan terakhir gunung Kelud, telah dipasang kamera CCTV untuk mengamati kawah danau dan sekitarnya. Akan tetapi, kamera pengamat mengalami gangguan dikarenakan adanya kabut. Tidak hanya itu, penghilangan kabut dari satu citra dengan struktur yang rumit, efek halo, dan distorsi warna adalah hal yang menantang teknik image recovery. Penelitian ini bertujuan mereduksi kabut dan meningkatkan visibilitas dari citra berkabut. Pada artikel ini, diusulkan metode dehazing baru yang menggabungkan metode Color Attenuation Prior (CAP) dan Adaptive Gamma Correction (AGC). Metode ini dibagi menjadi tiga modul utama, yaitu modul estimasi kedalaman (DispE), modul peningkatan peta transmisi (TME), dan modul restorasi (ImRec). Modul DispE yang diusulkan memanfaatkan teknik estimasi kedalaman dari CAP. Sedangkan modul TME mengadopsi teknik AGC. Dengan demikian, efek halo pada citra dapat dihindari dan estimasi peta transmisi yang efektif dapat dicapai. Selanjutnya, modul ImRec menggunakan peta transmisi hasil keluaran dari TME untuk memperbaiki distorsi warna citra kawah. Hasil eksperimental menunjukkan bahwa metode yang diusulkan bisa mengurangi kabut tanpa menimbulkan efek halo dan distorsi warna. Penelitian berikutnya difokuskan pada metode berbasis pembelajaran mesin.

Kata kunci: adaptive gamma correction, color attenuation prior, dehazing, kabut.

 

Abstract

[Single Kelud Volcano Lake Crater Image Dehazing Using Color Attenuation Prior and Adaptive Gamma Correction] Visibility of outdoor images captured in bad weather often decreases due to fog, sandstorms, and so on. Poor visibility, caused by atmospheric phenomena, is a factor in the failure of computer vision applications, such as external object recognition systems, obstacle detection systems or video surveillance systems. Due to the last eruption, CCTV cameras have been installed on top of Mt. Kelud summit to observe the crater of the lake and its surroundings. However, the observation camera experienced interference due to the fog. Not only that, the removal of fog from an image with complicated structures, halo effect, and color distortion is challenging image recovery techniques. This study aims to reduce the fog and improve the visibility of the foggy image. In this article, a new dehazing method is proposed that combines the Color Attenuation Prior (CAP) and Adaptive Gamma Correction (AGC) methods. This is divided into three main modules, namely the depth estimation module (DispE), the transmission map enhancement module (TME), and the restoration module (ImRec). The proposed DispE module utilizes depth estimation techniques from CAP. While the TME module adopts the AGC technique. Thus, the halo effect on the image can be avoided and the estimation of an effective transmission map can be achieved. Furthermore, the ImRec module uses a transmission map output from TME to correct the color distortion of the crater image. Experimental results show that the proposed method can reduce haze without causing halo and color distortion effects. Subsequent research focused on machine learning based methods.

Keywords: adaptive gamma correction, color attenuation prior, dark channel prior, dehazing, haze.

Keywords

adaptive gamma correction, color attenuation prior, dehazing, kabut.

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