Refinement of Distorted Images using Cycle-GAN

In a specific field of computer vision, image distortion and image degradation are major issues, particularly in applications that require accurate data fed directly from the camera. To address these problems, this proposed work employs a more complex approach, specifically a Cycle-Consistent Generative Adversarial Network (Cycle-GAN). Cycle-GAN can be used for image enhancement tasks. Current methods often rely on traditional interpolation techniques or simplistic convolutional neural networks (CNNs), which struggle to capture complex image features and may result in a loss of detail or the introduction of artifacts. To address these limitations, this paper proposes a novel approach integrating Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), leveraging the discriminative power of CNNs and the generative capabilities of GANs to effectively refine images while preserving fine details and minimizing artifacts. The Cycle-GAN model comprises two main components: the Generator and the Discriminator. The discriminator, constructed from convolutional layers, Leaky ReLU, and instance normalization, applies the PatchGAN strategy, where the model maps the input image sections as real or fake, emphasizing the image’s high-frequency details and texture characteristics. We conduct a thorough review of standard datasets and show that our method achieves a PSNR of 49.56 and an SSIM of 0.896, thus making it a reliable and practical choice for real-life image restoration problems. Finally, this paper aims to improve efficiency in removing image distortion and making images clearer.