Images captured in surveillance systems suffer from low contrast and faint color. Recently, plenty of dehazing algorithms have been proposed to enhance visibility and restore color. We present a new image enhancement algorithm based on multi-scale block-rooting processing. The basic idea is to apply the frequency domain image enhancement approach for different image block scales. The parameter of transform coefficient enhancement for every block is driven through optimization of measure of enhancement. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than the original image. To test the performance of the proposed algorithm, the public database O-HAZE is used.

No abstract

An image haze removal algorithm based on multiscale block-rooting processing has been developed for removing haze/fog from an image based on frequency-domain coefficient correction of a set of images followed by their fusion based on the Laplacian pyramid. A new stage is proposed in obtaining a local-global estimate of high-contrast images, which are also used in the general fusion model. The proposed block-based multiscale enhancement method base on 3-D block-rooting multiscale transform domain technique, comprising: finding similar blocks in the image by block-matching; block-grouping for different block sizes; applying 3-D block-matching parametric image enhancement; calculating the quality measure of enhancement; optimizing parameters of image enhancement method through the quality measure of enhancement; fusing different enhanced images. To test the performance of the proposed algorithm, the public database O-HAZE is used.