Hardware Implementation of a Fast and Efficient Haze Removal Method

Shiau, Yeu-Horng; Yang, Hung‐Yu; Chen, Pei-Yin; Chuang, Ya-Zhu

doi:10.1109/tcsvt.2013.2243650

Cited by 74 publications

(37 citation statements)

References 13 publications

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“…In order to evaluate the performance of the proposed method, we compare the proposed method with Shiau's method [5]. As shown in Fig.2, the proposed method preserves more details in complexity texture regions, compared with conventional method.…”

Section: Resultsmentioning

confidence: 99%

An efficient dehazing method for edge enhancement by using entropy-map

Park

Kim

Moon

2015

2015 IEEE International Conference on Consumer Electronics (ICCE)

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In this paper, we propose a novel dehazing method for effectively enhancing complex texture regions by using entropy information. The proposed method improves a drawback of an existing method due to estimating an incorrect transmission map. The proposed method consisted of two steps. In the first step, for effectively estimating a transmission map, an entropy map is generated from YCbCr color space. Then, by applying a threshold value to the entropy map, an input image is divided into complex texture regions and others. In the second step, in order to generate a transmission map, an averaging filter is applied to complex texture regions. And two edge filters are applied to non-texture regions. Consequently, a correct transmission map preserving more details in complex texture regions is generated. Experimental results have shown that the proposed method achieves better performance of image dehazing than existing methods.

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Section: Resultsmentioning

confidence: 99%

An efficient dehazing method for edge enhancement by using entropy-map

Park

Kim

Moon

2015

2015 IEEE International Conference on Consumer Electronics (ICCE)

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“…Zhu et al estimate transmission according to the relationship between brightness and saturation of the hazy images, yet they set a uniform scattering coefficient, which is apt to local insufficient dehazing effects. Shiau et al employ an extremum approximate method and an edge preserving mean filter to estimate atmospheric light and transmission, respectively, and design an 11‐stage pipelined hardware architecture to reduce computing time, but color distortion still exists.…”

Section: Related Workmentioning

confidence: 99%

Sky detection‐ and texture smoothing‐based high‐visibility haze removal from images and videos

Liu

Shen

Shao

et al. 2017

Computer Animation & Virtual

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To address the gloomy sky and the low contrast caused by the left fog in the existing image dehazing methods, we propose a robust haze removal algorithm for images and videos. First, a sky detection-based adaptive atmospheric light estimation method is designed for brighter and cleaner restoration results for the sky regions.Second, in order to reconstruct a transmission map in line with the depth variation, we preprocess the input image with texture smoothing to keep the color consistency inside the same planar object and devise a texture smoothing-based robust transmission estimation method, with which the contrast and color saturation of fog-free image are greatly promoted. Finally, the restored results are post-processed with the joint bilateral filter for the purpose of noise removal. What's more, a guided filter-based temporally coherent atmospheric light smoothing strategy and a Gaussian filter-based spatial-temporally coherent transmission smoothing strategy are put forward for video dehazing, which can ensure the spatial as well as temporal continuity of the haze-free videos. Experimental results show that the recovered haze-free images and videos have high contrast and color saturation with cleaner sky regions, and the haze-free videos are free of jittering and flickering phenomena.

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“…• Computational photography processor [31] using joint bilateral filtering [32] • Dehazing processor [33] with dark channel prior [34] • Field-programmable gate array (FPGA) implementation of stereo matching algorithms with AD census [35,36], etc.…”

Section: Circuit Implementation Of Sensing Systemmentioning

confidence: 99%

Recent progress in the technology linking sensors and digital circuits

Ikebe

2014

IEICE Electron. Express

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Sensing systems perform digital-signal processing after converting analog input to digital output. These systems are used for various applications. This paper addresses the link between sensors and digital circuits. In particular, I address two areas: analog-todigital converter (ADC) architecture and signal processing for sensing systems, and then describe their demands. Each circuit and system processing method was investigated and their basic outlines are illustrated. Finally, recent trends in specific hardware implementations of these algorithms are discussed.

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Hardware Implementation of a Fast and Efficient Haze Removal Method

Cited by 74 publications

References 13 publications

An efficient dehazing method for edge enhancement by using entropy-map

An efficient dehazing method for edge enhancement by using entropy-map

Sky detection‐ and texture smoothing‐based high‐visibility haze removal from images and videos

Recent progress in the technology linking sensors and digital circuits

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