Abstract

Images acquired under sand-dust weather conditions are severely degraded, with low contrast and severe color shift. The reason is that, due to the influence of sand-dust particles, light is scattered and absorbed, resulting in a blurred image and low contrast; the color shift is caused by the rapid attenuation of blue light. Therefore, to solve the problem of color shift and poor visibility in sand-dust images, this paper proposes a sand-dust image restoration method based on reversing the blue channel prior (RBCP). Under the influence of the blue channel, the dark channel prior (DCP) method will fail. Therefore, the method first reverses the blue channel of the sand-dust image and uses the dark channel prior method, which we call RBCP, and then, RBCP is used to estimate the atmospheric light and transmission map and recover the sand-dust image. The restored image shows significantly improved visibility. When estimating the transmission map, a guiding filter is used to improve the coarse transmission map, and a tolerance mechanism is introduced to modify the transmission map of bright areas in the sky to solve the problem of distortion in the sky. Finally, combined with the gray world, an adaptive color adjustment factor is introduced into the restoration model to remove the color shift. Experimental results via qualitative and quantitative evaluation demonstrate that the proposed method can effectively recover clear sand-dust images and produce results superior to those of other state-of-the-art methods.

Highlights

  • Most outdoor vision applications, such as intelligent surveillance [1], autonomous navigation [2], and vehicle tracking and monitoring systems [3], require clear visibility of the input image

  • To solve the problem of color shift and low contrast of sand-dust images, based on dark channel prior (DCP), this paper proposes a sand-dust image restoration method based on reversing the blue channel prior

  • We propose an effective method for sand-dust image restoration

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Summary

Introduction

Most outdoor vision applications, such as intelligent surveillance [1], autonomous navigation [2], and vehicle tracking and monitoring systems [3], require clear visibility of the input image. From the perspective of image restoration, Yu et al [21] proposed a single sand-dust degraded image restoration algorithm based on an atmospheric scattering model and information loss constraint, which can improve the contrast of different kinds of sand-dust degraded images This method accurately estimates the atmospheric light by iterating atmospheric light to compensate for the color shift and achieve color fidelity; this brings the problem of halos. Ren et al [27] proposed an algorithm consisting of a coarse-scale net that predicts a holistic transmission map based on an entire image and a fine-scale net that refines results locally This method can effectively improve the sharpness of a sand-dust image but cannot solve the color shift problem.

Restoration Based on DCP
Details of Our Method
Guided Filter-Improved Transmission
Atmospheric Scattering Model Restoration
Restoration Model After Improvement of the Sky Area Transmission Map
Recovery Model After Color Correction
Qualitative Evaluation
Quantitative Evaluation
Application 1 Underwater Image Restoration
Application 2 Image Segmentation
Shortcomings of Our Algorithm
Findings
Conclusion
Full Text
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