Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids

Li, Zhengguo; Shu, Haiyan; Zheng, Chaobing

doi:10.1109/tip.2021.3123551

Cited by 54 publications

(26 citation statements)

References 38 publications

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“…Our proposed method is the fastest among all methods and all image sizes and can be implemented in realtime applications. Even on 8K images, it only takes 1.5 seconds on average, about 15 times faster than the second-fastest Li et al [21]. It is noted that the fast image dehazing method proposed by Wang et al [14] can also achieve very high computational processing speed as fast as the proposed method.…”

Section: Time Assessmentsmentioning

confidence: 84%

“…In this section, Figs. 4-10 represent the performance comparison of our proposed dehazing method (MCPA) with some upto-date state-of-the-art methods, including FVR [59], DCP [6], BCCR [12], ATM [13], CAP [24], DehazeNet [39], PAD-MEF [19], Zhu et al [20], Zhao et al [22], and Li et al [21].…”

Section: Resultsmentioning

confidence: 99%

“…In Table 2 and Fig. 11, the average running time of FVR [59], DCP [6] (accelerated by the guided image filtering [57]), BCCR [12], ATM [13], CAP [24], DehazeNet [39], PAD-MEF [19], Zhu et al [20], Li et al [21], and Zhao et al [22] are evaluated. The state-of-the-art methods in the experiment use the official open-source codes of the Matlab version and use the default parameters to compare estimated time consumption.…”

Section: Time Assessmentsmentioning

confidence: 99%

“…Pixelwise weight maps were established using global and local exposures to guide the fusion process based on gamma-corrected underexposed images. Li et al [21] proposed globally guided image filtering to preserve the fine structure of the dehazed image. Experimental results showed that the proposed haze removal method can improve visual quality.…”

Section: Introductionmentioning

confidence: 99%

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MCPA: A Fast Single Image Haze Removal Method Based on the Minimum Channel and Patchless Approach

Fuh

Tung

2022

IEEE Access

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The image haze removal algorithm is challenging regarding computational processing speed and the hazy removal effect. Instead of using the local patch approach, which assumes the scene transmission to be locally constant and uses various filters to smooth the transmission map, this paper proposes a fast single image haze removal method based on a minimum channel and patchless approach. A new simple approach to estimate the atmospheric light and the scene transmission is proposed based on the minimum channel of images. The histogram of the minimum channel of the image is used to extract the atmospheric light pixels and exclude the non-hazy bright pixels in the image. The histogram equalization and image multiplication are applied to achieve better visual quality. In order to verify the performance of the proposed method, 100 images are collected from datasets I-HAZE, O-HAZE, and websites. Experimental results show that our proposed method outperforms up-to-date state-of-the-art haze removal algorithms using quantitative evaluations. From subjective comparisons, the proposed method outperforms most current haze removal algorithms in color restoration. Also, time assessment results show that our proposed method is the fastest among the up-to-date state-of-the-art haze removal methods and is about 15 times faster than the secondfastest method. The main contribution of the proposed method is significantly reducing computation time because it uses a patchless approach that does not need any filter and complicated algorithms. In addition to significantly reducing the computational processing speed, our proposed method can achieve better visual quality.

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Section: Time Assessmentsmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

Section: Time Assessmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MCPA: A Fast Single Image Haze Removal Method Based on the Minimum Channel and Patchless Approach

Fuh

Tung

2022

IEEE Access

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“…Deep CNNs have become the main-stream solution to many tasks, such as super-resolution [46], [47], classification [48] and dehazing [49], [50], etc. For the first time, Galliani et al [26] proposed a pioneering CNN model for SSR.…”

Section: B Cnn-based Modelsmentioning

confidence: 99%

HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution

Wu¹,

Li²,

Song³

et al. 2021

Preprint

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Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart. Due to the one-to-many nature of the SSR problem, a single RGB image can be reprojected to many HSIs. The key to tackle this illposed problem is to plug into multi-source prior information such as the natural RGB spatial context-prior, deep feature-prior or inherent HSI statistical-prior, etc., so as to improve the confidence and fidelity of reconstructed spectra. However, most current approaches only consider the general and limited priors in their designing the customized convolutional neural networks (CNNs), which leads to the inability to effectively alleviate the degree of ill-posedness. To address the problematic issues, we propose a novel holistic prior-embedded relation network (HPRN) for SSR. Basically, the core framework is delicately assembled by several multi-residual relation blocks (MRBs) that fully facilitate the transmission and utilization of the low-frequency content prior of RGB signals. Innovatively, the semantic prior of RGB input is introduced to identify category attributes and a semantic-driven spatial relation module (SSRM) is put forward to perform the feature aggregation among the clustered similar characteristics using a semantic-embedded relation matrix. Additionally, we develop a transformer-based channel relation module (TCRM), which breaks the habit of employing scalars as the descriptors of channel-wise relations in the previous deep feature-prior and replaces them with certain vectors, together with Transformerstyle feature interactions, supporting the representations to be more discriminative. In order to maintain the mathematical correlation and spectral consistency between hyperspectral bands, the second-order prior constraints (SOPC) are incorporated into the loss function to guide the HSI reconstruction process. Finally, extensive experimental results on four established benchmarks demonstrate that our HPRN can reach the state-of-the-art performance for SSR quantitatively and qualitatively and also, the effectiveness and usefulness of the reconstructed spectrum are verified by the classification results on the remote sensing dataset. Codes are available at https://github.com/Deep-imagelab/HPRN.

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