Task-Oriented Network for Image Dehazing

Lee

et al. 2021

Sensors

Image acquisition is a complex process that is affected by a wide variety of internal and environmental factors. Hence, visibility restoration is crucial for many high-level applications in photography and computer vision. This paper provides a systematic review and meta-analysis of visibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.

Section: Deep Learningmentioning

confidence: 99%

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Section: Training Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Visibility Restoration: A Systematic Review and Meta-Analysis

Lee

et al. 2021

Sensors

“…Moreover, to restore the binocular hazy image pairs, a binocular image dehazing Network (BidNet) was developed by Pang et al in [43], which can explore the correlations between the binocular image pairs to improve the recovery quality. Li et al [44] developed a spatially variant recurrent unit to make a coarse estimation of haze-free image. The coarse estimation is then refined to remove residual haze.…”

Section: B Image Dehazing Based On Learningmentioning

confidence: 99%

Single Image Dehazing Using Wavelet-Based Haze-Lines and Denoising

Hsu

Chen

2021

IEEE Access

Haze reduces the contrast of an image and causes the loss in colors, which has a negative effect on the subsequent object detection; therefore, single image dehazing is a challenging visual task. In addition, defects exist in previous existing dehazing approaches: Pixel-based dehazing approaches are likely to result in insufficient information to estimate the transmission, whereas patch-based ones are prone to generate shadows. They both also tend to induce color deviations. Therefore, this study proposes a novel method based on multi-scale wavelet and non-local dehazing. A hazy image is first decomposed into a low-frequency and three high-frequency sub-images by wavelet transform. Non-local dehazing and wavelet denoising are then employed on the low-frequency and high-frequency sub-images to remove the haze and noise, respectively. Finally, a haze-free image is obtained from the reconstruction of sub-images. The proposed method focuses on the dehazing and denoising on the low-frequency and high-frequency images respectively, through which the details on the image can be well preserved. Experimental results indicate that the proposed method is superior to the state-of-the-art approaches in both quantitative and qualitative evaluation on the synthetic and real-world image datasets.INDEX TERMS Single image dehazing, multi-scale wavelet, non-local dehazing, wavelet denoising

“…Recent studies leveraged efficient encoder—decoder frameworks and more sophisticated loss functions to improve the estimation accuracy. Li et al [ 24 ] exploited the encoder–decoder framework to develop a task-oriented network for haze removal, a refinement network for haze residual compensation, and a fusion network for fusing the previous two networks’ results. They also employed a loss function consisting of the mean absolute error, total variation, and dual composition losses.…”

Section: Introductionmentioning

confidence: 99%

Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

Lee

Kang

2021

Sensors

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.