127 citations · 140 across the 10 of their papers we have counts for
12 papers · 1 filter
HNDiff: Haze-Noise Diffusion for Image Dehazing
Jin-Ting He, Fu-Jen Tsai, Yan-Tsung Peng +3
Existing diffusion-based methods have recently made significant progress in image dehazing. However, they typically neglect the physics of haze formation and reconstruct clean imag…
DDTNet: Degradation Disentanglement and Transfer Network for Test-Time All-in-One De-weathering Adaptation
Kuan-Hung Lin, Fu-Jen Tsai, Yan-Tsung Peng +3
All-in-one adverse weather image restoration aims to remove multiple degradations, such as rain, haze, and snow, using a single unified model. Despite their broad applicability, ex…
BlurDM: A Blur Diffusion Model for Image Deblurring
Jin-Ting He, Fu-Jen Tsai, Yan-Tsung Peng +3
Diffusion models show promise for dynamic scene deblurring; however, existing studies often fail to leverage the intrinsic nature of the blurring process within diffusion models, l…
PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing
Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin +1
Image dehazing aims to remove unwanted hazy artifacts in images. Although previous research has collected paired real-world hazy and haze-free images to improve dehazing models' pe…
TANet: Triplet Attention Network for All-In-One Adverse Weather Image Restoration
Hsing-Hua Wang, Fu-Jen Tsai, Yen-Yu Lin +1
Adverse weather image restoration aims to remove unwanted degraded artifacts, such as haze, rain, and snow, caused by adverse weather conditions. Existing methods achieve remarkabl…
Domain-adaptive Video Deblurring via Test-time Blurring
Jin-Ting He, Fu-Jen Tsai, Jia-Hao Wu +4
Dynamic scene video deblurring aims to remove undesirable blurry artifacts captured during the exposure process. Although previous video deblurring methods have achieved impressive…
PANet: A Physics-guided Parametric Augmentation Net for Image Dehazing by Hazing
Chih-Ling Chang, Fu-Jen Tsai, Zi-Ling Huang +2
Image dehazing faces challenges when dealing with hazy images in real-world scenarios. A huge domain gap between synthetic and real-world haze images degrades dehazing performance…
ViStripformer: A Token-Efficient Transformer for Versatile Video Restoration
Fu-Jen Tsai, Yan-Tsung Peng, Chen-Yu Chang +4
Video restoration is a low-level vision task that seeks to restore clean, sharp videos from quality-degraded frames. One would use the temporal information from adjacent frames to…
ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation
Jia-Hao Wu, Fu-Jen Tsai, Yan-Tsung Peng +3
Image deblurring aims to remove undesired blurs from an image captured in a dynamic scene. Much research has been dedicated to improving deblurring performance through model archit…
Meta Transferring for Deblurring
Po-Sheng Liu, Fu-Jen Tsai, Yan-Tsung Peng +3
Most previous deblurring methods were built with a generic model trained on blurred images and their sharp counterparts. However, these approaches might have sub-optimal deblurring…
Stripformer: Strip Transformer for Fast Image Deblurring
Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin +2
Images taken in dynamic scenes may contain unwanted motion blur, which significantly degrades visual quality. Such blur causes short- and long-range region-specific smoothing artif…
BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring
Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin +2
Image motion blur results from a combination of object motions and camera shakes, and such blurring effect is generally directional and non-uniform. Previous research attempted to…