This the working repository for the paper entitled " A BLENDED DEEP LEARNING APPROACH FOR OUTDOOR SCENE IMAGE ENHANCEMENT"
Doi for the paper: 10.21817/indjcse/2022/v13i1/221301082

Abstract for the paper:
The most significant issue with visibility in outdoor images is atmospheric haze and inadequate lighting. If the haze is dense and the lighting is uneven, the problem becomes more difficult, resulting in poor image contrast. This paper provides an integrated method using Contrast Limited Adaptive Histogram Equalization(CLAHE) and AOD-net machine learning algorithm to reduce haze and enhance contrast of image. As images are pre-processed with CLAHE, a lightweight model efficiently dehazes the image. Experiments are conducted utilizing a variety of hazy pictures obtained from RESIDE-β dataset, and performance of proposed method was checked by PSNR, SSIM, PIQE, NIQE, BRISQUE, Entropy metrics. Results reveal that the suggested technique can successfully restore the contrast of haze affected pictures by dehazing them.