Colorizing a grayscale image is inherently an ill-posed problem with multi-modal
uncertainty. Language-based colorization offers a natural way of interaction to reduce such …

Image denoising is a fundamental problem in computer vision and multimedia computation.
Non-local filters are effective for image denoising. But existing deep learning methods that …

Colorization in monochrome-color camera systems aims to colorize the gray image from the
monochrome camera using the color image from the color camera as reference. Since …

The mono-color dual-lens system widely exists in the smartphone that captures asymmetric
stereo image pairs, including high-resolution (HR) monochrome images and low-resolution …

Recent advances in deep learning have brought new opportunities for analyzing land
dynamics, and Recurrent Neural Networks (RNNs) presented great potential in predicting …

We study the high dynamic range (HDR) imaging problem in dual-lens systems. Existing
methods usually treat the HDR imaging problem as an image fusion problem and the HDR …

We study the colorization problem in monochrome-color dual-lens camera systems, ie
colorizing the gray image from the monochrome camera using the color image from the color …

Abstract We study the High Dynamic Range (HDR) imaging problem using two Low
Dynamic Range (LDR) images that are shot from dual-lens systems in a single shot time …

Semantic image editing requires inpainting pixels following a semantic map. It is a
challenging task since this inpainting requires both harmony with the context and strict …

Image colorization, a pivotal aspect of computer vision, employs advanced algorithms to
transform grayscale images into realistic colors. This task is inherently challenging due to …