Results 71 to 80 of about 6,499 (150)
Enhancing convolutional neural network generalizability via low‐rank weight approximation
IET Image Processing, Volume 20, Issue 1, January/December 2026.A self‐supervised framework is proposed for image denoising based on the Tucker low‐rank tensor approximation. With the proposed design, we are able to characterize our denoiser with fewer parameters and train it based on a single image, which considerably improves the model's generalizability and reduces the cost of data acquisition. Abstract Noise is
Chenyin Gao, Shu Yang, Anru R. Zhang
wiley +1 more source
Perceptual quality evaluation for motion deblurring
IET Computer Vision, 2018 Motion deblurring has been widely studied. However, the relevant quality evaluation of motion deblurred images remains an open problem. The motion deblurred images are usually contaminated by noise, ringing and residual blur (NRRB) simultaneously ...
Bo Hu, Leida Li, Jiansheng Qian
doaj +1 more source
Plant Methods, 2023 Background Efficient and site-specific weed management is a critical step in many agricultural tasks. Image captures from drones and modern machine learning based computer vision methods can be used to assess weed infestation in agricultural fields more ...
Nikita Genze +5 more
doaj +1 more source
Optimized Pre-Compensating Compression
, 2018 In imaging systems, following acquisition, an image/video is transmitted or stored and eventually presented to human observers using different and often imperfect display devices.
Bruckstein, Alfred M. +2 more
core +1 more source
IET Image Processing, Volume 20, Issue 1, January/December 2026.This paper proposes a low‐light image enhancement and denoising algorithm tailored for tunnel scenes based on computer vision and deep learning technologies. On this basis, a tunnel pedestrian detection method based on connected domain dynamic threshold segmentation is designed, which can reduce the computational resources for identifying pedestrian ...
Yudan Tian +4 more
wiley +1 more source
Improved Deep Multi-Patch Hierarchical Network With Nested Module for Dynamic Scene Deblurring
IEEE Access, 2020 Dynamic scene deblurring is a significant technique in the field of computer vision. The multi-scale strategy has been successfully extended to the deep end-to-end learning-based deblurring task.
Zunjin Zhao +3 more
doaj +1 more source
Diffusion Models and Its Applications in Image Dehazing: A Survey
IET Image Processing, Volume 20, Issue 1, January/December 2026.1.This survey represents the first systematic and comprehensive overview of diffusion model‐based image dehazing, aiming to provide a valuable guide for future researchers and stimulate continued progress in this field. 2.We summarize relevant papers along with their corresponding code links and other resources for image dehazing and all‐in‐one image ...
Liangyu Zhu +6 more
wiley +1 more source
Blind UAV Images Deblurring Based on Discriminative Networks
Sensors, 2018 Unmanned aerial vehicles (UAVs) have become an important technology for acquiring high-resolution remote sensing images. Because most space optical imaging systems of UAVs work in environments affected by vibrations, the optical axis motion and image ...
Ruihua Wang +4 more
doaj +1 more source
, 2017 The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more challenging due to the blur varying spatially and the occlusions between the ...
A Chakrabarti +10 more
core +1 more source
IET Software, Volume 2026, Issue 1, 2026.This study uses advanced approaches on the enlarged BRATS dataset to increase brain magnetic resonance imaging (MRI) image reconstruction accuracy and reliability. This study addresses MRI image processing issues such as noise, artifacts, and high‐quality reconstruction. These traits are essential for brain tumor detection and analysis.
N. Sashi Prabha +2 more
wiley +1 more source