Improved single image dehazing using dark channel prior

An extraordinary challenge for real-world applications is traffic sign recognition, which plays a crucial role in driver guidance. Traffic signals are very difficult to detect using an extremely precise, real-time approach in practical autonomous driving scenes. This article reviews several object detection methods, including Yolo V3 and Densenet, in conjunction with spatial pyramid pooling (SPP). The SPP principle is employed to boost the Yolo V3 and Densenet backbone networks to extract the features. Moreover, we adopt spatial pyramid pooling to learn object features more completely. These models are measured and compared with key measurement parameters such as average accuracy (mAP), working area size, detection time, and billion floating-point number (BFLOPS). Based on the experimental results, Yolo V3 SPP outperforms state-of-the-art systems. Specifically, Yolo V3 SPP obtains 87.8% accuracy for small (S) target, 98.0% for medium (M) target, and 98.6% for large target groups in the BTSD dataset. Our results have shown that Yolo V3 SPP obtains the highest total BFLOPS (66.111), and mAP (99.28%). Consequently, SPP upgrades the achievement of all experimental models.

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“…Dilate is also valuable in joining broken 6) and the erosion operation is given in Eq. ( 7) (Fu et al 2015).…”

Section: Yolo V3 Spp Methodsmentioning

confidence: 99%

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Dewi

Chen

et al. 2021

J Ambient Intell Human Comput

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“…ConvNets explicitly assumes image inputs, making it possible to encode image properties such as texture and information content into the architecture. This feature makes the forward function in the architecture of ConvNet more efficient during implementation, thus reducing the number of parameters in the network [25]. The rest of the literature on the structure and architecture of Convolution Neural Network (CNNs/ConvNets) is widely presented in papers [26].…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Alenezi¹

2022

Computers, Materials &Amp; Continua

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Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method. The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions. This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks (CNNs) with a Parallax Attention Mechanism (PAM). It uses the differential pixel-based variance in order to estimate transmittance. The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map. The transmission map is also improved based on improved Markov random field (MRF) energy functions. We used different images to test the proposed algorithm. The entropy value of the proposed method was 7.43 and 7.39, a percent increase of 4.35% and 5.42%, respectively, compared to the best existing results. The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics. The proposed approach's drawback, an over-reliance on real ground truth images, is also investigated. The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.

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“…However, the larger the thread k is set, the greater amount of unnecessary calculation, but if the k is too small, GPU resources will be wasted. Therefore, a reasonable thread configuration is important to optimize the efficiency of the mean filter algorithm based on the integral image [35,36,40,41]. In this paper the thread number per block is set to 128 after evaluating a large number of experimental tests.…”

Section: Parallel Mean Filter Algorithm Based On Scanmentioning

confidence: 99%

Accelerating Haze Removal Algorithm Using CUDA

Wang

et al. 2020

Remote Sensing

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The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Graphics Processing Unit (GPU) accelerated parallel computing method for the real-time processing of high-definition video haze removal. First, based on the memory access pattern, we propose a simple but effective filter method called transposed filter combined with the fast local minimum filter algorithm and integral image algorithm. The proposed method successfully accelerates the parallel minimum filter algorithm and the parallel mean filter algorithm. Meanwhile, we adopt the inter-frame atmospheric light constraint to suppress the flicker noise in the video haze removal and simplify the estimation of atmospheric light. Experimental results show that our implementation can process the 1080p video sequence with 167 frames per second. Compared with single thread Central Processing Units (CPU) implementation, the speedup is up to 226× with asynchronous stream processing and qualified for the real-time high definition video haze removal.

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Improved single image dehazing using dark channel prior

Cited by 27 publications

References 15 publications

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Accelerating Haze Removal Algorithm Using CUDA

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