Empirical wavelet transform‐based fog removal via dark channel prior

Abstract

Haze and fog removing from videos and images has got massive concentration in the field of video and image processing because videos and images are severely affected by fog in tracking and surveillance system, object detection. Different defogging techniques proposed so far are based on polarisation, colour‐line model, anisotropic diffusion, dark channel prior (DCP) etc. However, these methods are unable to produce output image with desirable quality in the presence of dense fog and sky region. In this study, the authors have proposed a novel fog removal technique where DCP is applied on the low‐frequency component of empirical wavelet transformation coefficients of the foggy input image. They apply unsharp masking on wavelet coefficients of the embedded wavelet transformed image for improving the sharpness of the output image. Later contrast limited adaptive histogram equalisation technique is used as a post‐processing task to the inverse transformed image for producing the sharp and high contrast output. Finally, the colour and intensity of the contrast‐enhanced image are uplifted through S‐channel and V‐channel gain adjustment. The proposed method provides significant improvement to the overall quality of the output image compared to contemporary techniques. The quantitative and qualitative measurements confirm the claims.

Empirical Wavelet Transform Based ECG Signal Filtering Method

“…Terefore, the improvement in the segmentation of the spectra is crucial for the identifcation of various disturbances. It applies an adaptive flter that can be tuned according to the signal of interest and produces more accurate representations than traditional transform techniques, such as Fourier or wavelets [30][31][32][33][34][35]. As its name implies, this method relies on empirical data instead of predefned functions to produce its results.…”

“…Tis allows for improved fexibility in analyzing a wide variety of signals with minimal computational efort and complexity. Te EWT method assumes that the Fourier spectrum of the AM-FM component has a good stability [30][31][32][33][34][35]. Te bandpass flter set construction can be employed to segment and fltrate the spectra, and the signal can be decomposed adaptively via several modes.…”

“…To mitigate the impact of noise from various sources, the utilization of appropriate signal processing techniques becomes crucial. In this regard, the empirical wavelet transform (EWT) approaches [30,31] have emerged as efective solutions. Tis method combines the ideas of wavelet transform (WT), empirical mode decomposition (EMD), and Fourier transform to decompose signals into their constituents at diferent scales.…”

Adaptive Bright and Dark Channel Combined with Defogging Algorithm Based on Depth of Field

“…The enhanced defogging algorithm improves the image quality through image enhancement technology, mainly including adaptive histogram equalization [3,4], wavelet transform [5,6], homomorphic filtering [7], and Retinex enhancement [8][9][10] algorithms. The adaptive histogram equalization defogging algorithm [3,4] is an improvement on the basic histogram algorithm, which can indistinguishably improve the image contrast, suppress the slope of the transformation function to some extent, and avoid the phenomenon that rising too fast resulting weak image contrast and oversaturation.…”

“…However, such methods will amplify the noise in the image when there is a lot of noise in the image. The wavelet transform method divides the image into high-frequency region and low-frequency region and uses the enhancement method for the high-frequency region to achieve the purpose of image defogging by improving the image contrast [5,6], but it is not suitable for the situation of too bright or too dark and uneven illumination. The homomorphic filtering algorithm composes the illumination component and reflection component of the image, respectively, and processes them in the frequency domain, highlighting the details by enhancing the high-frequency information of the image [7].…”