Agrawal, NavneetJain, Eiti2020-09-172020-09-172019-06-11Jain E. And Agrawal N.https://krishikosh.egranth.ac.in/handle/1/5810151407A Hybrid Optimization of Dual Tree Complex Wavelet Transform for Enhancement of Low Light ImagesWith the popularity of digital cameras and smart phones, digital images play an important role in day to day life. However, when images are captured in low light environment, generally at night, the quality of image descends rapidly because of loss of contrast, low dynamic range and noise. Besides deteriorating visual quality of the images, this low light images also results in degrading the performance of many computer algorithm that are responsible for high quality input. Thus, image enhancement of low light images is very important. Contrast enhancement is one of the most essential and significant spatial based image enhancement technique. The main aim of this technique is to adjust the local contrast in the image and increase the dynamic range so to get the clear regions in an image. The contrast enhancement changes the intensity of the pixel for obtaining a more enhanced image. So it is required to improve the contrast in an image to get perceptually more pleasing or visually more informative vision effect. This thesis report presents an effective approach is required for maximizing the performance in low intensity scenarios. In this research work, we proposed hybrid optimization algorithm with Dual Tree Complex Wavelet Transform (DT-CWT) for enhancement of low light images. More concretely, low intensity image is preprocessed using Contrast Level Adaptive Histogram Equalization (CLAHE) and Bilateral Filtering. Furthermore, the complex wavelet transform (CWT) is performed for normalization which is followed by hybrid optimization of Ant Colony (ACO) and Particle Swarm intelligence (PSO). Optimization is done for achieving high peak signal to noise ratio and reducing error rate probability. The proposed method is evaluated on the basis of performance metrics such as Peak Signal to Noise ratio (PSNR), Mean Square Error (MSE), Discrete Entropy (DE), Absolute Mean Brightness Error (AMBE), Colorfulness.EnglishThesis