A Novel Unbiased Deep Learning Approach (DL-Net) in Feature Space for Converting Gray to Color Image

Abstract

Gray to Color conversion causes difficulties because of the nature of its intrinsic multi-modality. Despite recent significant advancements in this domain by numerous learning-based approaches, there still have two drawbacks: i) implausible color assignment and ii) contextual ambiguity. Recently deep learning models are being used for colorization as they outperform others. In a training image, desaturated color components are greater than saturated color components due to the larger background areas (clouds, pavement, dirt, walls, etc.) compared to the focused objects. This imbalanced feature representation biases the learning model in favor of major features. However, small regions with specific colors are the region of interest. To solve this problem, we proposed the Deep Localization Network (DL-Net) by modifying the mean squared error backpropagation algorithm. We compute chromatic component-based Local Losses (LLs) which are the primary component of the proposed DL-Net. The LL employs priority on rare semantic components of the original image features. It works to improve diverse-range dependency modeling in an effort to reduce contextual ambiguity and color leakage that promotes the production of more plausible coloring. With a number of current methodologies, we contrast our proposed approach. The experimental findings demonstrate that our proposed method produces good colorization of images and outperforms other methods in terms of SSIM, MSE, and PSNR quality criteria.