Guided image denoising recovers clean target images by fusing guidance images and noisy
target images. Several deep neural networks have been designed for this task, but they are …

In this paper, we introduce the single image glare removal (SIGR) task. SIGR aims to
eliminate glare or light caused by external environment in lighting scene. To efficiently …

Deep convolutional neural networks have been investigated for atmospheric particle
removal and accomplished the state-of-the-art performance. Most of the previous studies …

This study primarily presents efficient tools for optimization-based image processing using a
bilateral filter (BF). Generally, for image restoration, eg, deblurring, a forward operation and …

Pixel-domain weighting methods for multiple-exposure blending can efficiently remove
noise and under-/over-exposed pixels simultaneously in high dynamic range (HDR) image …

Deep convolutional neural networks have achieved state-of-the-art performance for the
removal of atmospheric obscuration. However, most relevant studies have focused on …

In this paper, we propose gradual flash fusion, a new imaging concept that enables
acquisition of pseudo multi-exposure images in a passive manner. This means that our …

Using a flash/no-flash image pair, we propose a novel white-balancing technique that can
effectively correct the color balance of a complex scene under multiple light sources. In the …

We propose a reference-based filtering method that transforms the local color distribution of
individual image patches. With our method, the color of a reference image is transformed by …

We present a new image integration technique for a flash and long-exposure image pair to
capture a dark scene without incurring blurring or noisy artifacts. Most existing methods …