An Effective Image Enhancement Algorithm for Single Image Haze Removal Based on Daubechies Wavelet Filter Bank

The primary issues in producing natural images are low contrast and poor quality. A novel approach for image enhancement is proposed in this work employing the discrete wavelet transform, adaptive thresholding, and morphology-based method. To start, pre-processing procedures are used to preserve the fine features of an image. After that, Daubechies-2 (tap-4) wavelet filter bank is used to split the image into high-pass and low-pass subband images. Images from the high-pass subband are improved using an adaptive thresholding technique. The morphology-based top-hat transform is used to improve low-pass subband images. Once the high-frequency and low-frequency subimages have been processed, the enhanced image can be obtained by utilizing the inverse DWT approach. PSNR and RMSE are used to assess the extent to which the suggested method performs. Experiments revealed that this technique is excellent at both enhancing an image’s details and effectively preserving its edge features.