The field of image processing is witnessing significant developments with the integration of wavelet transformations, enabling improved accuracy and efficiency in various applications. Recent research has focused on leveraging wavelet transforms to enhance image denoising, segmentation, and feature extraction. This has led to the development of innovative models and techniques that can adapt to different noise levels, camera settings, and user preferences. Notably, the use of wavelet domain fingerprints has streamlined the process of source camera identification, achieving higher detection accuracy and improved processing speed. Furthermore, wavelet-based methods have shown promise in capturing fine-grained details, leading to improved performance in image classification and anomaly detection tasks. Overall, these advancements are expected to have a significant impact on various fields, including forensic science, healthcare, and computer vision. Noteworthy papers include: The Frequency-enhanced Multi-granularity Context Network, which introduces a novel approach for efficient vertebrae segmentation, and the Biorthogonal Tunable Wavelet Unit, which enhances convolutional neural networks for improved image classification and anomaly detection. The Towards Controllable Real Image Denoising with Camera Parameters paper also highlights a controllable denoising framework that adaptively removes noise from images based on camera parameters.