Image Quality Analysis (IQA) is most significant concepts that is gaining attention among the researchers. Most images are considerably influenced by ambient lights. HumanVisual System perceives theimages quality in day and night time that causes the deprivation of luminance and features. In this article, we put forward an adaptive fuzzy-based DCNN technique that estimates the loss and enhancement of the images during seasonal changes. The natural scenery images are collected and preprocessed using Gaussian filter method that removes the unwanted noises. The preprocessed image is then guided with a guided filter method that helps to segment the seasonal changes of an image. The statistical features are extracted from the Adaptive Fuzzy-based Gamma Correction method that specifically leverages the gamma parameters using fuzzy-based decisional approach. Further on, the Deep Convolutional Neural Networks (DCNN) is running to classify the seasonal changes on images based on the computed image quality score. Investigational result have proven the accuracy of suggested technique in concerning of accuracy, precision and recall.

Haze is very apparent in images shot during periods of bad weather (fog). The image's clarity and readability are both diminished as a result. As part of this work, we suggest a method for improving the quality of the hazy image and for identifying any objects hidden inside it. To address this, we use the picture enhancement techniques of Dark Channel Prior and Guided Filter. The Saliency map is then used to segment the improved image and identify passing vehicles. Lastly, we describe our method for calculating the actual distance in units from a camera-equipped vehicle of an item (another vehicle).Our proposed solution can warn the driver based on the distance to help them prevent an accident. Our suggested technology improves images and accurately detects vehicles nearly 100% of the time.