WHFL: Wavelet-Domain High Frequency Loss for Sketch-to-Image Translation

Even a rough sketch can effectively convey the descriptions of objects, as humans can imagine the original shape from the sketch. The sketch-to-photo translation is a computer vision task that enables a machine to do this imagination, taking a binary sketch image and generating plausible RGB images corresponding to the sketch. Hence, deep neural networks for this task should learn to generate a wide range of frequencies because most parts of the input (binary sketch image) are composed of DC signals. In this paper, we propose a new loss function named Wavelet-domain High-Frequency Loss (WHFL) to overcome the limitations of previous methods that tend to have a bias toward low frequencies. The proposed method emphasizes the loss on the high frequencies by designing a new weight matrix imposing larger weights on the high bands. Unlike existing handcraft methods that control frequency weights using binary masks, we use the matrix with finely controlled elements according to frequency scales. The WHFL is designed in a multi-scale form, which lets the loss function focus more on the high frequency according to decomposition levels. We use the WHFL as a complementary loss in addition to conventional ones defined in the spatial domain. Experiments show we can improve the qualitative and quantitative results in both spatial and frequency domains. Additionally, we attempt to verify the WHFL’s high-frequency generation capability by defining a new evaluation metric named Unsigned Euclidean Distance Field Error (UEDFE).