An Empirical Study of the Effect of Single Image Dehazing on Object Detection

Jeong, Chi Yoon; Ha, Donghee; Moon, Kyeong-Deok; Kim, Mooseop

doi:10.5391/jkiis.2022.32.2.117

Cited by 1 publication

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“…However, these methods mainly focus on the DCP algorithm, which is a representative prior-knowledge-based method, and have limited dehazing performance with regard to PSNR and SSIM. A recent study [23] also pointed out that when the performance of the dehazing method is low, the performance of object detection, which is one of the major image analysis tasks, does not improve. Few studies [20,24] on lightweight dehazing networks have been conducted to speed up deep learning-based methods, but they did not use an end-to-end approach that directly generates a clean image from a source image.…”

Section: Introductionmentioning

confidence: 99%

An end-to-end deep learning approach for real-time single image dehazing

Jeong

Moon

Kim

2023

J Real-Time Image Proc

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Image dehazing methods can restore clean images from hazy images and are popularly used as a preprocessing step to improve performance in various image analysis tasks. In recent times, deep learning-based methods have been used to sharply increase the visual quality of restored images, but they require a long computation time. The processing time of image-dehazing methods is one of the important factors to be considered in order not to affect the latency of the main image analysis tasks such as detection and segmentation. We propose an end-to-end network model for real-time image dehazing. We devised a zoomed convolution group that processes computation-intensive operations with low resolution to decrease the processing time of the network model without performance degradation. Additionally, the zoomed convolution group adopts an efficient channel attention module to improve the performance of the network model. Thus, we designed a network model using a zoomed convolution group to progressively recover haze-free images using a coarse-to-fine strategy. By adjusting the sampling ratio and the number of convolution blocks that make up the convolution group, we distributed small and large computational complexities respectively in the early and later operational stages. The experimental results with the proposed method on a public dataset showed a real-time performance comparable to that of another state-of-the-art (SOTA) method. The proposed network’s peak-signal-to-noise ratio was 0.8 dB lower than that of the SOTA method, but the processing speed was 10.4 times faster.

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Section: Introductionmentioning

confidence: 99%