Results 71 to 80 of about 11,754,470 (203)
Rethinking Performance Gains in Image Dehazing Networks [PDF]
, 2022 Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning.
Du, Xin +3 more
core +1 more source
Electronics Letters, Volume 62, Issue 1, January/December 2026.PhysMamba‑Unroll, a framework that addresses the high computational cost of deploying deep unfolding networks on edge devices by synergistically integrating Mamba's linear complexity with physics‐informed unrolling. ABSTRACT Real‐time adverse weather image restoration on edge devices is hindered by a fundamental tension: Deep unfolding networks (DUNs ...
Yongkang Li +4 more
wiley +1 more source
IET Image Processing, Volume 20, Issue 1, January/December 2026.This paper proposes a novel image enhancement method, WCTE, which integrates Haar wavelet transform and adaptive CLAHE to improve the visibility of low‐contrast tablet images. Combined with the YOLOv11 model, this approach significantly boosts defect detection accuracy, especially for half‐grain and paste tabtal.
Zimei Tu +3 more
wiley +1 more source
Single image dehazing for real colored-haze images
, 2021 Since the problem of image dehazing is of great significance and challenging, it has attracted many researchers for a long time. Many excellent dehazing algorithms have emerged one after another.
Wu, Zijun
core +2 more sources
Progressive Knowledge Distillation for Edge‐Deployable Solder Joint Segmentation
IET Image Processing, Volume 20, Issue 1, January/December 2026.Solder‐Yolo is a lightweight deep learning model based on YOLOv8‐seg, designed for high‐precision solder joint inspection in FPC ribbon cables. It incorporates model pruning, knowledge distillation and a hierarchical context attention module to achieve 96.7% precision and 91.3% mAP while maintaining high inference speed.
Kunhong Li +4 more
wiley +1 more source
Color Channel Transfer for Image Dehazing
, 2019 In this letter we introduce a simple but effective concept, Color Channel Transfer (CCT), that is able to substantially improve the performance of various dehazing techniques.
Ancuti, Cosmin +3 more
core +1 more source
A GAN-Based Input-Size Flexibility Model for Single Image Dehazing [PDF]
, 2022 Image-to-image translation based on generative adversarial network (GAN) has achieved state-of-the-art performance in various image restoration applications.
Zhang, Yue +3 more
core +1 more source
Image dehazing using two‐dimensional canonical correlation analysis
IET Computer Vision, 2015 Image dehazing is an important issue that interests both image processing and computer vision. In this study, image dehazing is modelled as an example‐based learning problem, and a novel dehazing algorithm using two‐dimensional (2D) canonical correlation
Liqian Wang, Liang Xiao, Zhihui Wei
doaj +1 more source
IET Image Processing, Volume 20, Issue 1, January/December 2026.In this paper, we introduce a novel image dehazing algorithm based on dual‐channel prior adaptive contrast‐limited enhancement. The algorithm estimates model parameters from different perspectives based on dual‐channel prior knowledge and fuses the parameters according to the characteristics of each channel.
Chang Su +4 more
wiley +1 more source
Pre‐Trained Codebook‐Based Enhancement: A Novel Approach for Clarifying Underwater Images
IET Image Processing, Volume 20, Issue 1, January/December 2026.This work presents a codebook‐driven enhancement network to tackle colour distortion and detail loss in underwater images. By aligning multi‐scale features with a pre‐trained VQGAN codebook and fusing shallow‐to‐deep cues, the method boosts contrast, edges and clarity without requiring large paired datasets.
Yuanxue Xin +4 more
wiley +1 more source