iReWindColor v2: Cross-Window Enhanced Transformer for Point Interactive Image Colorization

Point-interactive image colorization is a task that enables users to add color to grayscale images by providing sparse color annotations (hints) at user-defined locations. These hints are then propagated across the image to generate a plausible colorization result with minimal user effort. While existing methods have demonstrated effectiveness in leveraging user input, they often suffer from excessive color diffusion and color bleeding beyond object boundaries, compromising visual fidelity. In this work, we present an extended version of our previous method, iReWindColor, which introduced a window-based Vision Transformer (ViT) framework for efficient and fine-grained colorization. The extended framework incorporates two key improvements. First, we enhance the Window Encoder by integrating Window-Connecting Convolution module, which enables information exchange across adjacent windows. This addition mitigates discontinuities at window boundaries and improves spatial coherence in the colorized output. Second, we provide a comprehensive ablation study to quantitatively assess the impact of each architectural component and design choice, offering deeper insights into the effectiveness of the proposed modifications. Our approach employs small patches to achieve detailed color propagation while maintaining computational efficiency through localized attention. The Residual Embedding mechanism remains integral to aggregating global context, whereas the proposed Encode Block, which combines Self-Attention and Convolutional Neural Networks (CNNs), strengthens inter-patch relationships and improves the utilization of user-provided hints. Experimental results on multiple benchmark datasets demonstrate that the extended model consistently outperforms the original iReWindColor as well as other state-of-the-art methods, producing visually coherent, user-aligned colorizations with improved computational efficiency.