Improved Single Image Dehazing Using Dark Channel Prior and Multi-scale Retinex

Abstract

In this paper, we propose an improved image dehazing algorithm using dark channel prior and Multi-Scale Retinex. Main improvement lies in automatic and fast acquisition of transmission map of the scene. We implement the Multi-scale Retinex algorithm on the luminance component in YCbCr space, obtain a pseudo transmission map whose function is similar to the transmission map in original approach. Combining with the haze image model and the dark channel prior, we can recover a high quality haze-free image. Compared with the original method, our algorithm has two main advantages: (i) no user interaction is needed, and (ii) restoring the image much faster while maintaining comparable dehazing performance.

A Review of Image Restoration based Image Defogging Algorithms

“…These methods improve the quality of image mainly by image contrast enhancement or color enhancement, and many algorithms have already been done using this criterion, such as histogram equalization algorithm [5] and multi-scale color image enhancement algorithm [6]. But this method may lead to loss of original part information, and the result of dehazing is distorted.…”

“…To improve the efficiency, Xie et al used the Multiscale Retinex algorithm to estimate the transmission [6].…”

Dehazing Algorithm Using Wavelength Compensation to Improve Visibility in Foggy Images

“…An artificial light is detected; the added luminance is to be eliminated. CA module recovers the true color of the scenes featuring wide range of weather conditions [7].This CA module determines the intensity statistics for the RGB color space of a captured image in order to acquire the color information. The last step of our process, the proposed VR module recovers a high quality free image.…”

A Comprehensive Study on Various Haze Removal and Visibility Restoration Techniques for Image Processing

“…In the proposed algorithm Multi-Scale Retinex algorithm implemented on the luminance component in YCbCr space .By combining with the haze image model and the dark channel prior, improve a high quality haze-free image compared with the original algorithm. Basic advantage of this algorithm is no user interaction is needed and much faster as compared with original [16]. He et al (2011) proposes an effective haze removal prior using dark Chanel prior method from a single input image.…”