Abstract

Image enhancement remains a fundamental challenge in computer vision, particularly in scenarios involving low contrast, uneven illumination, and noise interference. While traditional spatial and frequency domain techniques efficiently address specific distortions, they often fail to generalize across diverse image conditions. To overcome these limitations, this paper proposes an Adaptive Hybrid Image Enhancement Framework that integrates deep learning-based enhancement networks with classical filtering algorithms for optimal visual restoration and detail preservation. The proposed method employs a Convolutional Neural Network (CNN) enhanced with an attention-guided residual block to learn fine-grained illumination patterns, followed by adaptive fusion with traditional filters such as Gaussian smoothing, histogram equalization, and bilateral filtering. This hybrid approach ensures a balance between structural clarity and natural color consistency. A dynamic weighting mechanism is applied to adjust enhancement intensity based on local luminance and texture statistics. Experimental validation on benchmark datasets such as MIT-Adobe FiveK, BSD500, and LIME demonstrates significant improvement over state-of-the-art methods. The proposed hybrid model achieves an average PSNR of 32.8 dB, SSIM of 0.95, and naturalness index improvement of 18%, outperforming standalone deep learning and filtering techniques. The adaptive framework effectively enhances visibility in underexposed, blurred, and noisy conditions, making it ideal for applications in medical imaging, autonomous vision, and surveillance systems.